Science.gov

Sample records for model based diagnosis

  1. Efficient Model-Based Diagnosis Engine

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Vatan, Farrokh; Barrett, Anthony; James, Mark; Mackey, Ryan; Williams, Colin

    2009-01-01

    An efficient diagnosis engine - a combination of mathematical models and algorithms - has been developed for identifying faulty components in a possibly complex engineering system. This model-based diagnosis engine embodies a twofold approach to reducing, relative to prior model-based diagnosis engines, the amount of computation needed to perform a thorough, accurate diagnosis. The first part of the approach involves a reconstruction of the general diagnostic engine to reduce the complexity of the mathematical-model calculations and of the software needed to perform them. The second part of the approach involves algorithms for computing a minimal diagnosis (the term "minimal diagnosis" is defined below). A somewhat lengthy background discussion is prerequisite to a meaningful summary of the innovative aspects of the present efficient model-based diagnosis engine. In model-based diagnosis, the function of each component and the relationships among all the components of the engineering system to be diagnosed are represented as a logical system denoted the system description (SD). Hence, the expected normal behavior of the engineering system is the set of logical consequences of the SD. Faulty components lead to inconsistencies between the observed behaviors of the system and the SD (see figure). Diagnosis - the task of finding faulty components - is reduced to finding those components, the abnormalities of which could explain all the inconsistencies. The solution of the diagnosis problem should be a minimal diagnosis, which is a minimal set of faulty components. A minimal diagnosis stands in contradistinction to the trivial solution, in which all components are deemed to be faulty, and which, therefore, always explains all inconsistencies.

  2. Model-based reconfiguration: Diagnosis and recovery

    NASA Technical Reports Server (NTRS)

    Crow, Judy; Rushby, John

    1994-01-01

    We extend Reiter's general theory of model-based diagnosis to a theory of fault detection, identification, and reconfiguration (FDIR). The generality of Reiter's theory readily supports an extension in which the problem of reconfiguration is viewed as a close analog of the problem of diagnosis. Using a reconfiguration predicate 'rcfg' analogous to the abnormality predicate 'ab,' we derive a strategy for reconfiguration by transforming the corresponding strategy for diagnosis. There are two obvious benefits of this approach: algorithms for diagnosis can be exploited as algorithms for reconfiguration and we have a theoretical framework for an integrated approach to FDIR. As a first step toward realizing these benefits we show that a class of diagnosis engines can be used for reconfiguration and we discuss algorithms for integrated FDIR. We argue that integrating recovery and diagnosis is an essential next step if this technology is to be useful for practical applications.

  3. Fault diagnosis based on continuous simulation models

    NASA Technical Reports Server (NTRS)

    Feyock, Stefan

    1987-01-01

    The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.

  4. Qualitative model-based diagnosis using possibility theory

    NASA Technical Reports Server (NTRS)

    Joslyn, Cliff

    1994-01-01

    The potential for the use of possibility in the qualitative model-based diagnosis of spacecraft systems is described. The first sections of the paper briefly introduce the Model-Based Diagnostic (MBD) approach to spacecraft fault diagnosis; Qualitative Modeling (QM) methodologies; and the concepts of possibilistic modeling in the context of Generalized Information Theory (GIT). Then the necessary conditions for the applicability of possibilistic methods to qualitative MBD, and a number of potential directions for such an application, are described.

  5. Extending model-based diagnosis for analog thermodynamical devices

    NASA Technical Reports Server (NTRS)

    Rouquette, Nicolas; Chien, Steve; Robertson, Charles

    1993-01-01

    The increasing complexity of process control applications have posed difficult problems in fault detection, isolation, and recovery. Deep knowledge-based approaches, such as model-based diagnosis, have offered some promise in addressing these problems. However, the difficulties of adapting these techniques to situations involving numerical reasoning and noise have limited the applicability of these techniques. This paper describes an extension of classical model-based diagnosis techniques to deal with sparse data, noise, and complex noninvertible numerical models. These diagnosis techniques are being applied to the External Active Thermal Control System for Space Station Freedom.

  6. Skills Diagnosis Using IRT-Based Continuous Latent Trait Models

    ERIC Educational Resources Information Center

    Stout, William

    2007-01-01

    This article summarizes the continuous latent trait IRT approach to skills diagnosis as particularized by a representative variety of continuous latent trait models using item response functions (IRFs). First, several basic IRT-based continuous latent trait approaches are presented in some detail. Then a brief summary of estimation, model…

  7. The use of multiple models in case-based diagnosis

    NASA Technical Reports Server (NTRS)

    Karamouzis, Stamos T.; Feyock, Stefan

    1993-01-01

    The work described in this paper has as its goal the integration of a number of reasoning techniques into a unified intelligent information system that will aid flight crews with malfunction diagnosis and prognostication. One of these approaches involves using the extensive archive of information contained in aircraft accident reports along with various models of the aircraft as the basis for case-based reasoning about malfunctions. Case-based reasoning draws conclusions on the basis of similarities between the present situation and prior experience. We maintain that the ability of a CBR program to reason about physical systems is significantly enhanced by the addition to the CBR program of various models. This paper describes the diagnostic concepts implemented in a prototypical case based reasoner that operates in the domain of in-flight fault diagnosis, the various models used in conjunction with the reasoner's CBR component, and results from a preliminary evaluation.

  8. Model-based fault diagnosis in continuous dynamic systems.

    PubMed

    Lo, C H; Wong, Y K; Rad, A B

    2004-07-01

    Traditional fault detection and isolation methods are based on quantitative models which are sometimes difficult and costly to obtain. In this paper, qualitative bond graph (QBG) reasoning is adopted as the modeling scheme to generate a set of qualitative equations. The QBG method provides a unified approach for modeling engineering systems, in particular, mechatronic systems. An input-output qualitative equation derived from QBG formalism performs continuous system monitoring. Fault diagnosis is activated when a discrepancy is observed between measured abnormal behavior and predicted system behavior. Genetic algorithms (GA's) are then used to search for possible faulty components among a system of qualitative equations. In order to demonstrate the performance of the proposed algorithm, we have tested it on a laboratory scale servo-tank liquid process rig. Results of the proposed model-based fault detection and diagnosis algorithm for the process rig are presented and discussed.

  9. Model-based fault detection and diagnosis in ALMA subsystems

    NASA Astrophysics Data System (ADS)

    Ortiz, José; Carrasco, Rodrigo A.

    2016-07-01

    The Atacama Large Millimeter/submillimeter Array (ALMA) observatory, with its 66 individual telescopes and other central equipment, generates a massive set of monitoring data every day, collecting information on the performance of a variety of critical and complex electrical, electronic and mechanical components. This data is crucial for most troubleshooting efforts performed by engineering teams. More than 5 years of accumulated data and expertise allow for a more systematic approach to fault detection and diagnosis. This paper presents model-based fault detection and diagnosis techniques to support corrective and predictive maintenance in a 24/7 minimum-downtime observatory.

  10. Model-based diagnosis of a carbon dioxide removal assembly

    NASA Astrophysics Data System (ADS)

    Throop, David R.; Scarl, Ethan A.

    1992-03-01

    Model-based diagnosis (MBD) has been applied to a variety of mechanisms, but few of these have been in fluid flow domains. Important mechanism variables in these domains are continuous, and the mechanisms commonly contain complex recycle patterns. These properties violate some of the common assumptions for MBD. The CO2 removal assembly (CDRA) for the cabin atmosphere aboard NASA's Space Station Freedom is such a mechanism. Early work on diagnosing similar mechanisms showed that purely associative diagnostic systems could not adequately handle these mechanisms' frequent reconfigurations. This suggested a model-based approach and KATE was adapted to the domain. KATE is a constraint-based MBD shell. It has been successfully applied to liquid flow problems in handling liquid oxygen. However, that domain does not involve complex recycle streams, but the CDRA does. KATE had solved constraint sets by propagating parameter values through constraints; this method often fails on constraints sets which describe recycle systems. KATE was therefore extended to allow it to use external algebraic programs to solve its constraint sets. This paper describes the representational challenges involved in that extension, and describes adaptions which allowed KATE to work within the representational limitations imposed by those algebraic programs. It also presents preliminary results of the CDRA modeling.

  11. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

    PubMed Central

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  12. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory.

    PubMed

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-18

    Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.

  13. An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Narasimhan, Sriram; Roychoudhury, Indranil; Daigle, Matthew; Pulido, Belarmino

    2013-01-01

    Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.

  14. Fault detection and diagnosis based on modeling and estimation methods.

    PubMed

    Huang, Sunan; Tan, Kok Kiong

    2009-05-01

    This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the fault function. The fault model obtained by the second neural network (NN) can be used for identifying the failure mode by comparing it with any known failure modes. Finally, a simulation example is presented to illustrate the effectiveness of the proposed scheme.

  15. Model-based monitoring and diagnosis of a satellite-based instrument

    NASA Technical Reports Server (NTRS)

    Bos, Andre; Callies, Jorg; Lefebvre, Alain

    1995-01-01

    For about a decade model-based reasoning has been propounded by a number of researchers. Maybe one of the most convincing arguments in favor of this kind of reasoning has been given by Davis in his paper on diagnosis from first principles (Davis 1984). Following their guidelines we have developed a system to verify the behavior of a satellite-based instrument GOME (which will be measuring Ozone concentrations in the near future (1995)). We start by giving a description of model-based monitoring. Besides recognizing that something is wrong, we also like to find the cause for misbehaving automatically. Therefore, we show how the monitoring technique can be extended to model-based diagnosis.

  16. FY90 Based Cost Models to Support Diagnosis Related Management

    DTIC Science & Technology

    1992-08-04

    models presented here were based upon the final model formhs selected for use with the FY88 data. With the exception of identifying outlying fa- cilities...CENTER EXPENSE MODELS ARMY ALV SASER IC NONCLINICIAN EXPENSES 2 1 4 7 CLINICIAN EXPENSES 6 4 6 16 AMBULATORY EXPENSES 0 2 4 6 TOTAL EXPENSES* 1 0 2 3

  17. Model-Based Diagnosis in a Power Distribution Test-Bed

    NASA Technical Reports Server (NTRS)

    Scarl, E.; McCall, K.

    1998-01-01

    The Rodon model-based diagnosis shell was applied to a breadboard test-bed, modeling an automated power distribution system. The constraint-based modeling paradigm and diagnostic algorithm were found to adequately represent the selected set of test scenarios.

  18. Implementation of a model based fault detection and diagnosis technique for actuation faults of the SSME

    NASA Technical Reports Server (NTRS)

    Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

    1991-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the Space Shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the Space Shuttle Main Engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  19. An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Daigle, Matthew J.; Roychoudhury, Indranil

    2012-01-01

    Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

  20. Diagnosis by integrating model-based reasoning with knowledge-based reasoning

    NASA Technical Reports Server (NTRS)

    Bylander, Tom

    1988-01-01

    Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.

  1. Online model-based diagnosis to support autonomous operation of an advanced life support system

    NASA Technical Reports Server (NTRS)

    Biswas, Gautam; Manders, Eric-Jan; Ramirez, John; Mahadevan, Nagabhusan; Abdelwahed, Sherif

    2004-01-01

    This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.

  2. Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft

    NASA Astrophysics Data System (ADS)

    Desbazeille, M.; Randall, R. B.; Guillet, F.; El Badaoui, M.; Hoisnard, C.

    2010-07-01

    This work aims at monitoring large diesel engines by analyzing the crankshaft angular speed variations. It focuses on a powerful 20-cylinder diesel engine with crankshaft natural frequencies within the operating speed range. First, the angular speed variations are modeled at the crankshaft free end. This includes modeling both the crankshaft dynamical behavior and the excitation torques. As the engine is very large, the first crankshaft torsional modes are in the low frequency range. A model with the assumption of a flexible crankshaft is required. The excitation torques depend on the in-cylinder pressure curve. The latter is modeled with a phenomenological model. Mechanical and combustion parameters of the model are optimized with the help of actual data. Then, an automated diagnosis based on an artificially intelligent system is proposed. Neural networks are used for pattern recognition of the angular speed waveforms in normal and faulty conditions. Reference patterns required in the training phase are computed with the model, calibrated using a small number of actual measurements. Promising results are obtained. An experimental fuel leakage fault is successfully diagnosed, including detection and localization of the faulty cylinder, as well as the approximation of the fault severity.

  3. HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Brownston, Lee

    2012-01-01

    Hybrid Diagnosis Engine (HyDE) is a general framework for stochastic and hybrid model-based diagnosis that offers flexibility to the diagnosis application designer. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. HyDE is a general framework for stochastic hybrid model-based diagnosis of discrete faults; that is, spontaneous changes in operating modes of components. HyDE combines ideas from consistency-based and stochastic approaches to model- based diagnosis using discrete and continuous models to create a flexible and extensible architecture for stochastic and hybrid diagnosis. HyDE supports the use of multiple paradigms and is extensible to support new paradigms. HyDE generates candidate diagnoses and checks them for consistency with the observations. It uses hybrid models built by the users and sensor data from the system to deduce the state of the system over time, including changes in state indicative of faults. At each time step when observations are available, HyDE checks each existing candidate for continued consistency with the new observations. If the candidate is consistent, it continues to remain in the candidate set. If it is not consistent, then the information about the inconsistency is used to generate successor candidates while discarding the candidate that was inconsistent. The models used by HyDE are similar to simulation models. They describe the expected behavior of the system under nominal and fault conditions. The model can be constructed in modular and hierarchical fashion by building component/subsystem models (which may themselves contain component/ subsystem models) and linking them through shared variables/parameters. The

  4. Fault diagnosis using noise modeling and a new artificial immune system based algorithm

    NASA Astrophysics Data System (ADS)

    Abbasi, Farshid; Mojtahedi, Alireza; Ettefagh, Mir Mohammad

    2015-12-01

    A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

  5. Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Lo, Chun; Lynch, Jerome P.; Liu, Mingyan

    2016-01-01

    Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.

  6. Observer based on-line fault diagnosis of continuous systems modeled as Petri nets.

    PubMed

    Renganathan, K; Bhaskar, Vidhyacharan

    2010-10-01

    This paper describes a technique for achieving on-line fault diagnosis in continuous systems that are modeled using Petri nets. The effect of place markings and transition markings are considered and based on the computed error between the initial marking and subsequent markings evolved in time, the faults are categorized assuming that the markings are both observable and unobservable. An algorithm has been suitably proposed for achieving detection of faults for a typical continuous three tank system along with suitable results.

  7. Livingstone Model-Based Diagnosis of Earth Observing One Infusion Experiment

    NASA Technical Reports Server (NTRS)

    Hayden, Sandra C.; Sweet, Adam J.; Christa, Scott E.

    2004-01-01

    The Earth Observing One satellite, launched in November 2000, is an active earth science observation platform. This paper reports on the progress of an infusion experiment in which the Livingstone 2 Model-Based Diagnostic engine is deployed on Earth Observing One, demonstrating the capability to monitor the nominal operation of the spacecraft under command of an on-board planner, and demonstrating on-board diagnosis of spacecraft failures. Design and development of the experiment, specification and validation of diagnostic scenarios, characterization of performance results and benefits of the model- based approach are presented.

  8. A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

    NASA Astrophysics Data System (ADS)

    Al-Bugharbee, Hussein; Trendafilova, Irina

    2016-05-01

    This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pretreatment allows the use of a linear time invariant autoregressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasises the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal. The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process.

  9. Model-Based Diagnosis and Prognosis of a Water Recycling System

    NASA Technical Reports Server (NTRS)

    Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

    2013-01-01

    A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

  10. Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Roychoudhury, Indranil; Balaban, Edward; Saxena, Abhinav

    2010-01-01

    Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.

  11. The KATE shell: An implementation of model-based control, monitor and diagnosis

    NASA Technical Reports Server (NTRS)

    Cornell, Matthew

    1987-01-01

    The conventional control and monitor software currently used by the Space Center for Space Shuttle processing has many limitations such as high maintenance costs, limited diagnostic capabilities and simulation support. These limitations have caused the development of a knowledge based (or model based) shell to generically control and monitor electro-mechanical systems. The knowledge base describes the system's structure and function and is used by a software shell to do real time constraints checking, low level control of components, diagnosis of detected faults, sensor validation, automatic generation of schematic diagrams and automatic recovery from failures. This approach is more versatile and more powerful than the conventional hard coded approach and offers many advantages over it, although, for systems which require high speed reaction times or aren't well understood, knowledge based control and monitor systems may not be appropriate.

  12. Explanation Constraint Programming for Model-based Diagnosis of Engineered Systems

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Brownston, Lee; Burrows, Daniel

    2004-01-01

    We can expect to see an increase in the deployment of unmanned air and land vehicles for autonomous exploration of space. In order to maintain autonomous control of such systems, it is essential to track the current state of the system. When the system includes safety-critical components, failures or faults in the system must be diagnosed as quickly as possible, and their effects compensated for so that control and safety are maintained under a variety of fault conditions. The Livingstone fault diagnosis and recovery kernel and its temporal extension L2 are examples of model-based reasoning engines for health management. Livingstone has been shown to be effective, it is in demand, and it is being further developed. It was part of the successful Remote Agent demonstration on Deep Space One in 1999. It has been and is being utilized by several projects involving groups from various NASA centers, including the In Situ Propellant Production (ISPP) simulation at Kennedy Space Center, the X-34 and X-37 experimental reusable launch vehicle missions, Techsat-21, and advanced life support projects. Model-based and consistency-based diagnostic systems like Livingstone work only with discrete and finite domain models. When quantitative and continuous behaviors are involved, these are abstracted to discrete form using some mapping. This mapping from the quantitative domain to the qualitative domain is sometimes very involved and requires the design of highly sophisticated and complex monitors. We propose a diagnostic methodology that deals directly with quantitative models and behaviors, thereby mitigating the need for these sophisticated mappings. Our work brings together ideas from model-based diagnosis systems like Livingstone and concurrent constraint programming concepts. The system uses explanations derived from the propagation of quantitative constraints to generate conflicts. Fast conflict generation algorithms are used to generate and maintain multiple candidates

  13. Model-based cardiovascular disease diagnosis: a preliminary in-silico study.

    PubMed

    Ebrahimi Nejad, Shiva; Carey, Jason P; McMurtry, M Sean; Hahn, Jin-Oh

    2017-04-01

    In this study, we developed and examined the feasibility of a model-based system identification approach to cardiovascular disease diagnosis. The basic premise of the approach is that it may be possible to diagnose cardiovascular disease from disease-induced alterations in the arterial mechanical properties manifested in the proximal and distal arterial blood pressure waveforms. It first individualizes the lumped-parameter model of wave propagation and reflection in the artery using the measurement of proximal and distal arterial blood pressure waveforms. Then, it employs a diagnosis logic, in the form of disease-specific patterns in model parameters, referred as [Formula: see text] and pulse transit time. The longitudinal change in these parameters is used to diagnose the presence of peripheral artery disease and arterial stiffening. We illustrated the feasibility of the proposed approach by testing it in a full-scale in-silico arterial tree simulation. The results showed that the approach exhibited superior sensitivity to ankle-brachial index and convenience to carotid-femoral pulse wave velocity: The model parameters [Formula: see text] and [Formula: see text] responded with up to 100 and 40 % changes to peripheral artery disease with up to 50 % arterial blockage whereas the change in ankle-brachial index was [Formula: see text]; the same parameters responded with up to 300 and 40 % changes to up to 100 % arterial stiffening while pulse transit time changed by up to 24 %. Together with the development of more convenient techniques for the measurement of arterial blood pressure waveforms, the proposed approach may evolve into a viable alternative to the state-of-the-art techniques for cardiovascular disease diagnosis.

  14. Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Shibin; Huang, Weiguo; Zhu, Z. K.

    2011-05-01

    At constant rotating speed, localized faults in rotating machine tend to result in periodic shocks and thus arouse periodic transients in the vibration signal. The transient feature analysis has always been a crucial problem for localized fault detection, and the key aim for transient feature analysis is to identify the model and its parameters (frequency, damping ratio and time index) of the transient, and the time interval, i.e. period, between transients. Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection. With the proposed method, both parameters of a single transient and the period between transients can be identified from the vibration signal, and localized faults can be detected based on the parameters, especially the period. First, a simulation signal is used to test the performance of the proposed method. Then the method is applied to the vibration signals of different types of bearings with localized faults in the outer race, the inner race and the rolling element, respectively, and all the results show that the period between transients, representing the localized fault characteristic, is successfully detected. The method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period. Moreover, it can be drawn that for bearing fault detection, the single-side wavelet model is more suitable than double-side one, while the double-side model for gearbox fault detection. This research proposed an effective method of localized fault detection for rotating machine fault diagnosis through transient modeling and parameter detection.

  15. Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

    NASA Technical Reports Server (NTRS)

    Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

    1992-01-01

    In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

  16. Improved automated diagnosis of misfire in internal combustion engines based on simulation models

    NASA Astrophysics Data System (ADS)

    Chen, Jian; Bond Randall, Robert

    2015-12-01

    In this paper, a new advance in the application of Artificial Neural Networks (ANNs) to the automated diagnosis of misfires in Internal Combustion engines(IC engines) is detailed. The automated diagnostic system comprises three stages: fault detection, fault localization and fault severity identification. Particularly, in the severity identification stage, separate Multi-Layer Perceptron networks (MLPs) with saturating linear transfer functions were designed for individual speed conditions, so they could achieve finer classification. In order to obtain sufficient data for the network training, numerical simulation was used to simulate different ranges of misfires in the engine. The simulation models need to be updated and evaluated using experimental data, so a series of experiments were first carried out on the engine test rig to capture the vibration signals for both normal condition and with a range of misfires. Two methods were used for the misfire diagnosis: one is based on the torsional vibration signals of the crankshaft and the other on the angular acceleration signals (rotational motion) of the engine block. Following the signal processing of the experimental and simulation signals, the best features were selected as the inputs to ANN networks. The ANN systems were trained using only the simulated data and tested using real experimental cases, indicating that the simulation model can be used for a wider range of faults for which it can still be considered valid. The final results have shown that the diagnostic system based on simulation can efficiently diagnose misfire, including location and severity.

  17. Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yan, Ruqiang

    2016-12-01

    The bearing failure, generating harmful vibrations, is one of the most frequent reasons for machine breakdowns. Thus, performing bearing fault diagnosis is an essential procedure to improve the reliability of the mechanical system and reduce its operating expenses. Most of the previous studies focused on rolling bearing fault diagnosis could be categorized into two main families, kurtosis-based filter method and wavelet-based shrinkage method. Although tremendous progresses have been made, their effectiveness suffers from three potential drawbacks: firstly, fault information is often decomposed into proximal frequency bands and results in impulsive feature frequency band splitting (IFFBS) phenomenon, which significantly degrades the performance of capturing the optimal information band; secondly, noise energy spreads throughout all frequency bins and contaminates fault information in the information band, especially under the heavy noisy circumstance; thirdly, wavelet coefficients are shrunk equally to satisfy the sparsity constraints and most of the feature information energy are thus eliminated unreasonably. Therefore, exploiting two pieces of prior information (i.e., one is that the coefficient sequences of fault information in the wavelet basis is sparse, and the other is that the kurtosis of the envelope spectrum could evaluate accurately the information capacity of rolling bearing faults), a novel weighted sparse model and its corresponding framework for bearing fault diagnosis is proposed in this paper, coined KurWSD. KurWSD formulates the prior information into weighted sparse regularization terms and then obtains a nonsmooth convex optimization problem. The alternating direction method of multipliers (ADMM) is sequentially employed to solve this problem and the fault information is extracted through the estimated wavelet coefficients. Compared with state-of-the-art methods, KurWSD overcomes the three drawbacks and utilizes the advantages of both family

  18. Applying Model-based Diagnosis to a Rapid Propellant Loading System

    NASA Technical Reports Server (NTRS)

    Goodrich, Charlie H.; Narasimhan, Sriram; Daigle, Matthew J.; Hatfield, Walter H.; Johnson, Robert G.

    2009-01-01

    The overall objective of the US Air Force Research Laboratory (AFRL) Rapid Propellant Loading (RPL) Program is to develop a launch vehicle, payload and ground support equipment that can support a rapid propellant load and launch within one hour. NASA Kennedy Space Center (KSC) has been funded by AFRL to develop hardware and software to demonstrate this capability. The key features of the software would be the ability to recognize and adapt to failures in the physical hardware components, advise operators of equipment faults and workarounds, and put the system in a safe configuration if unable to fly. In December 2008 NASA KSC and NASA Ames Research Center (ARC) demonstrated model based simulation and diagnosis capabilities for a scaled-down configuration of the RPL hardware. In this paper we present a description of the model-based technologies that were included as part of this demonstration and the results that were achieved. In continuation of this work we are currently testing the technologies on a simulation of the complete RPL system. Later in the year, when the RPL hardware is ready, we will be integrating these technologies with the real-time operation of the system to provide live state estimates. In future years we will be developing the capability to recover from faulty conditions via redundancy and reconfiguration.

  19. A Memory Based Model of Posttraumatic Stress Disorder: Evaluating Basic Assumptions Underlying the PTSD Diagnosis

    PubMed Central

    Rubin, David C.; Berntsen, Dorthe; Johansen, Malene Klindt

    2009-01-01

    In the mnemonic model of PTSD, the current memory of a negative event, not the event itself determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the DSM. The model accounts for important and reliable findings that are often inconsistent with the current diagnostic view and that have been neglected by theoretical accounts of the disorder, including the following observations. The diagnosis needs objective information about the trauma and peritraumatic emotions, but uses retrospective memory reports that can have substantial biases. Negative events and emotions that do not satisfy the current diagnostic criteria for a trauma can be followed by symptoms that would otherwise qualify for PTSD. Predisposing factors that affect the current memory have large effects on symptoms. The inability-to-recall-an-important-aspect-of-the-trauma symptom does not correlate with other symptoms. Loss or enhancement of the trauma memory affects PTSD symptoms in predictable ways. Special mechanisms that apply only to traumatic memories are not needed, increasing parsimony and the knowledge that can be applied to understanding PTSD. PMID:18954211

  20. Variable-Length Computerized Adaptive Testing Based on Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Hsu, Chia-Ling; Wang, Wen-Chung; Chen, Shu-Ying

    2013-01-01

    Interest in developing computerized adaptive testing (CAT) under cognitive diagnosis models (CDMs) has increased recently. CAT algorithms that use a fixed-length termination rule frequently lead to different degrees of measurement precision for different examinees. Fixed precision, in which the examinees receive the same degree of measurement…

  1. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    PubMed

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

  2. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

    PubMed Central

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  3. An evidential reasoning extension to quantitative model-based failure diagnosis

    NASA Technical Reports Server (NTRS)

    Gertler, Janos J.; Anderson, Kenneth C.

    1992-01-01

    The detection and diagnosis of failures in physical systems characterized by continuous-time operation are studied. A quantitative diagnostic methodology has been developed that utilizes the mathematical model of the physical system. On the basis of the latter, diagnostic models are derived each of which comprises a set of orthogonal parity equations. To improve the robustness of the algorithm, several models may be used in parallel, providing potentially incomplete and/or conflicting inferences. Dempster's rule of combination is used to integrate evidence from the different models. The basic probability measures are assigned utilizing quantitative information extracted from the mathematical model and from online computation performed therewith.

  4. An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease.

    PubMed

    Chiang, Hsiu-Sen; Pao, Shun-Chi

    2016-05-01

    Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early treatment of Alzheimer's disease can significantly reduce deterioration. Early diagnosis is difficult, and early symptoms are frequently overlooked. While much of the literature focuses on disease detection, the use of electroencephalography (EEG) in Alzheimer's diagnosis has received relatively little attention. This study combines the fuzzy and associative Petri net methodologies to develop a model for the effective and objective detection of Alzheimer's disease. Differences in EEG patterns between normal subjects and Alzheimer patients are used to establish prediction criteria for Alzheimer's disease, potentially providing physicians with a reference for early diagnosis, allowing for early action to delay the disease progression.

  5. Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

    PubMed

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-05-05

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects.

  6. Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

    PubMed Central

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-01-01

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

  7. Completing fault models for abductive diagnosis

    SciTech Connect

    Knill, E.; Cox, P.T.; Pietrzykowski, T.

    1992-11-05

    In logic-based diagnosis, the consistency-based method is used to determine the possible sets of faulty devices. If the fault models of the devices are incomplete or nondeterministic, then this method does not necessarily yield abductive explanations of system behavior. Such explanations give additional information about faulty behavior and can be used for prediction. Unfortunately, system descriptions for the consistency-based method are often not suitable for abductive diagnosis. Methods for completing the fault models for abductive diagnosis have been suggested informally by Poole and by Cox et al. Here we formalize these methods by introducing a standard form for system descriptions. The properties of these methods are determined in relation to consistency-based diagnosis and compared to other ideas for integrating consistency-based and abductive diagnosis.

  8. A Memory-Based Model of Posttraumatic Stress Disorder: Evaluating Basic Assumptions Underlying the PTSD Diagnosis

    ERIC Educational Resources Information Center

    Rubin, David C.; Berntsen, Dorthe; Bohni, Malene Klindt

    2008-01-01

    In the mnemonic model of posttraumatic stress disorder (PTSD), the current memory of a negative event, not the event itself, determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the "Diagnostic and Statistical Manual of Mental Disorders" (4th ed., text rev.; American Psychiatric Association,…

  9. Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model

    PubMed Central

    Lee, So Mi; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

    2015-01-01

    Objective To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). Materials and Methods From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Results Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Conclusion Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology. PMID:26576128

  10. Combining FDI and AI approaches within causal-model-based diagnosis.

    PubMed

    Gentil, Sylviane; Montmain, Jacky; Combastel, Christophe

    2004-10-01

    This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.

  11. Immunity-based diagnosis for a motherboard.

    PubMed

    Shida, Haruki; Okamoto, Takeshi; Ishida, Yoshiteru

    2011-01-01

    We have utilized immunity-based diagnosis to detect abnormal behavior of components on a motherboard. The immunity-based diagnostic model monitors voltages of some components, CPU temperatures, and fan speeds. We simulated abnormal behaviors of some components on the motherboard, and we utilized the immunity-based diagnostic model to evaluate motherboard sensors in two experiments. These experiments showed that the immunity-based diagnostic model was an effective method for detecting abnormal behavior of components on the motherboard.

  12. Fault diagnosis in gas turbines using a model-based technique

    NASA Astrophysics Data System (ADS)

    Merrington, G. L.

    1994-04-01

    Reliable methods for diagnosing faults and detecting degraded performance in gas turbine engines are continually being sought. In this paper, a model-based technique is applied to the problem of detecting degraded performance in a military turbofan engine from take-off acceleration-type transients. In the past, difficulty has been experienced in isolating the effects of some of the physical processes involved. One such effect is the influence of the bulk metal temperature on the measured engine parameters during large power excursions. It will be shown that the model-based technique provides a simple and convenient way of separating this effect from the faster dynamic components. The important conclusion from this work is that good fault coverage can be gleaned from the resultant pseudo-steady-state gain estimates derived in this way.

  13. Diagnosis of pulmonary hypertension from magnetic resonance imaging–based computational models and decision tree analysis

    PubMed Central

    Swift, Andrew J.; Capener, David; Kiely, David; Hose, Rod; Wild, Jim M.

    2016-01-01

    Abstract Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH. PMID:27252844

  14. Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test

    NASA Astrophysics Data System (ADS)

    Wang, Xiyang; Makis, Viliam

    2009-11-01

    Vibration behavior induced by gear shaft crack is different from that induced by gear tooth crack. Hence, a fault indicator used to detect tooth damage may not be effective for monitoring shaft condition. This paper proposes an autoregressive model-based technique to detect the occurrence and advancement of gear shaft cracks. An autoregressive model is fitted to the time synchronously averaged signal of the gear shaft in its healthy state. The order of the autoregressive model is selected using Akaike information criterion and the coefficient estimates are obtained by solving the Yule-Walker equations with the Levinson-Durbin recursion algorithm. The established autoregressive model is then used as a linear prediction filter to process the future signal. The Kolmogorov-Smirnov test is applied on line for the prediction of error signals. The calculated distance is used as a fault indicator and its capability to diagnose shaft crack effectively is demonstrated using a full lifetime gear shaft vibration data history. The other frequently used statistical measures such as kurtosis and variance are also calculated and the results are compared with the Kolmogorov-Smirnov test.

  15. Knowledge-based diagnosis for aerospace systems

    NASA Technical Reports Server (NTRS)

    Atkinson, David J.

    1988-01-01

    The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

  16. Improving Distributed Diagnosis Through Structural Model Decomposition

    NASA Technical Reports Server (NTRS)

    Bregon, Anibal; Daigle, Matthew John; Roychoudhury, Indranil; Biswas, Gautam; Koutsoukos, Xenofon; Pulido, Belarmino

    2011-01-01

    Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals computed by extending Possible Conflicts to build local event-based diagnosers based on global diagnosability analysis. The proposed approach is applied to a multitank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches.

  17. Physically-based modeling of speed sensors for fault diagnosis and fault tolerant control in wind turbines

    NASA Astrophysics Data System (ADS)

    Weber, Wolfgang; Jungjohann, Jonas; Schulte, Horst

    2014-12-01

    In this paper, a generic physically-based modeling framework for encoder type speed sensors is derived. The consideration takes into account the nominal fault-free and two most relevant fault cases. The advantage of this approach is a reconstruction of the output waveforms in dependence of the internal physical parameter changes which enables a more accurate diagnosis and identification of faulty incremental encoders i.a. in wind turbines. The objectives are to describe the effect of the tilt and eccentric of the encoder disk on the digital output signals and the influence of the accuracy of the speed measurement in wind turbines. Simulation results show the applicability and effectiveness of the proposed approach.

  18. Model-Based Fault Diagnosis: Performing Root Cause and Impact Analyses in Real Time

    NASA Technical Reports Server (NTRS)

    Figueroa, Jorge F.; Walker, Mark G.; Kapadia, Ravi; Morris, Jonathan

    2012-01-01

    Generic, object-oriented fault models, built according to causal-directed graph theory, have been integrated into an overall software architecture dedicated to monitoring and predicting the health of mission- critical systems. Processing over the generic fault models is triggered by event detection logic that is defined according to the specific functional requirements of the system and its components. Once triggered, the fault models provide an automated way for performing both upstream root cause analysis (RCA), and for predicting downstream effects or impact analysis. The methodology has been applied to integrated system health management (ISHM) implementations at NASA SSC's Rocket Engine Test Stands (RETS).

  19. The AIMAR recommendations for early diagnosis of chronic obstructive respiratory disease based on the WHO/GARD model*.

    PubMed

    Nardini, Stefano; Annesi-Maesano, Isabella; Del Donno, Mario; Delucchi, Maurizio; Bettoncelli, Germano; Lamberti, Vincenzo; Patera, Carlo; Polverino, Mario; Russo, Antonio; Santoriello, Carlo; Soverina, Patrizio

    2014-01-01

    to the Italian context; the document of the Agency for Regional Healthcare Services (AGE.NA.S) is a more suited compendium for consultation, and the recent joint statement on integrated COPD management of the three major Italian scientific Associations in the respiratory area together with the contribution of a Society of General Medicine deals prevalently with some critical issues (appropriateness of diagnosis, pharmacological treatment, rehabilitation, continuing care); also the document "Care Continuity: Chronic Obstructive Pulmonary Disease (COPD)" of the Global Alliance against chronic Respiratory Diseases (GARD)-Italy does not treat in depth the issue of early diagnosis. The present document - produced by the AIMAR (Interdisciplinary Association for Research in Lung Disease) Task Force for early diagnosis of chronic respiratory disease based on the WHO/GARD model and on available evidence and expertise -after a general examination of the main epidemiologic aspects, proposes to integrate the above-mentioned existing documents. In particular: a) it formally indicates on the basis of the available evidence the modalities and the instruments necessary for carrying out secondary prevention at the primary care level (a pro-active,'case-finding'approach; assessment of the individual's level of risk of COPD; use of short questionnaires for an initial screening based on symptoms; use of simple spirometry for the second level of screening); b) it identifies possible ways of including these activities within primary care practice; c) it places early diagnosis within the "systemic", consequential management of chronic respiratory diseases, which will be briefly described with the aid of schemes taken from the Italian and international reference documents.

  20. The AIMAR recommendations for early diagnosis of chronic obstructive respiratory disease based on the WHO/GARD model*

    PubMed Central

    2014-01-01

    the Italian context; the document of the Agency for Regional Healthcare Services (AGE.NA.S) is a more suited compendium for consultation, and the recent joint statement on integrated COPD management of the three major Italian scientific Associations in the respiratory area together with the contribution of a Society of General Medicine deals prevalently with some critical issues (appropriateness of diagnosis, pharmacological treatment, rehabilitation, continuing care); also the document “Care Continuity: Chronic Obstructive Pulmonary Disease (COPD)” of the Global Alliance against chronic Respiratory Diseases (GARD)-Italy does not treat in depth the issue of early diagnosis. The present document – produced by the AIMAR (Interdisciplinary Association for Research in Lung Disease) Task Force for early diagnosis of chronic respiratory disease based on the WHO/GARD model and on available evidence and expertise –after a general examination of the main epidemiologic aspects, proposes to integrate the above-mentioned existing documents. In particular: a) it formally indicates on the basis of the available evidence the modalities and the instruments necessary for carrying out secondary prevention at the primary care level (a pro-active,‘case-finding’approach; assessment of the individual’s level of risk of COPD; use of short questionnaires for an initial screening based on symptoms; use of simple spirometry for the second level of screening); b) it identifies possible ways of including these activities within primary care practice; c) it places early diagnosis within the “systemic”, consequential management of chronic respiratory diseases, which will be briefly described with the aid of schemes taken from the Italian and international reference documents. PMID:25473523

  1. Final Progress Report on Model-Based Diagnosis of Soil Limitations to Forest Productivity

    SciTech Connect

    Luxmoore, R.J.

    2004-08-30

    This project was undertaken in support of the forest industry to link modeling of nutrients and productivity with field research to identify methods for enhancing soil quality and forest productivity and for alleviating soil limitations to sustainable forest productivity. The project consisted of a series of related tasks, including (1) simulation of changes in biomass and soil carbon with nitrogen fertilization, (2) development of spreadsheet modeling tools for soil nutrient availability and tree nutrient requirements, (3) additional modeling studies, and (4) evaluation of factors involved in the establishment and productivity of southern pine plantations in seasonally wet soils. This report also describes the two Web sites that were developed from the research to assist forest managers with nutrient management of Douglas-fir and loblolly pine plantations.

  2. Applying Model-Based Diagnosis to a Rapid Propellant Loading System (Postprint)

    DTIC Science & Technology

    2009-06-01

    exception is significant, diagnose the problem, and decide whether or not to continue the countdown. KATE is a generic software shell for performing...Knowledge Based Expert System for Propellant System Monitoring at the Kennedy Space Center. In Proceedings of the 22nd Space Congress. Cocoa Beach, FL

  3. Parameter estimation for uncertain systems based on fault diagnosis using Takagi-Sugeno model.

    PubMed

    Nagy-Kiss, A M; Schutz, G; Ragot, J

    2015-05-01

    The paper addresses a systematic procedure to deal with state and parameter uncertainty estimation for nonlinear time-varying systems. A robust observer with respect to states, inputs and perturbations is designed, using a Takagi-Sugeno (T-S) approach with unknown premise variables. Tools of the linear automatic to the nonlinear systems are applied, using the Linear Matrix Inequalities optimization. The observer estimates the uncertainties, the states and minimizes the effect of external disturbances on the estimation error. The uncertainties are modelled in a polynomial way which allows considering the uncertainty estimation as a fault detection problem. The residual sensitivity to faults while maintaining robustness according to a noise signal is handled by H∞/H- approach. The method performance is illustrated using the three-tank system.

  4. Knowledge-based nursing diagnosis

    NASA Astrophysics Data System (ADS)

    Roy, Claudette; Hay, D. Robert

    1991-03-01

    Nursing diagnosis is an integral part of the nursing process and determines the interventions leading to outcomes for which the nurse is accountable. Diagnoses under the time constraints of modern nursing can benefit from a computer assist. A knowledge-based engineering approach was developed to address these problems. A number of problems were addressed during system design to make the system practical extended beyond capture of knowledge. The issues involved in implementing a professional knowledge base in a clinical setting are discussed. System functions, structure, interfaces, health care environment, and terminology and taxonomy are discussed. An integrated system concept from assessment through intervention and evaluation is outlined.

  5. A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

    NASA Astrophysics Data System (ADS)

    Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2016-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

  6. A resampling-based Markovian model for automated colon cancer diagnosis.

    PubMed

    Ozdemir, Erdem; Sokmensuer, Cenk; Gunduz-Demir, Cigdem

    2012-01-01

    In recent years, there has been a great effort in the research of implementing automated diagnostic systems for tissue images. One major challenge in this implementation is to design systems that are robust to image variations. In order to meet this challenge, it is important to learn the systems on a large number of labeled images from a different range of variation. However, acquiring labeled images is quite difficult in this domain, and hence, the labeled training data are typically very limited. Although the issue of having limited labeled data is acknowledged by many researchers, it has rarely been considered in the system design. This paper successfully addresses this issue, introducing a new resampling framework to simulate variations in tissue images. This framework generates multiple sequences from an image for its representation and models them using a Markov process. Working with colon tissue images, our experiments show that this framework increases the generalization capacity of a learner by increasing the size and variation of the training data and improves the classification performance of a given image by combining the decisions obtained on its sequences.

  7. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.

    PubMed

    Zhang, B; Liang, X L; Gao, H Y; Ye, L S; Wang, Y G

    2016-05-13

    We evaluated the application of three machine learning algorithms, including logistic regression, support vector machine and back-propagation neural network, for diagnosing congenital heart disease and colorectal cancer. By inspecting related serum tumor marker levels in colorectal cancer patients and healthy subjects, early diagnosis models for colorectal cancer were built using three machine learning algorithms to assess their corresponding diagnostic values. Except for serum alpha-fetoprotein, the levels of 11 other serum markers of patients in the colorectal cancer group were higher than those in the benign colorectal cancer group (P < 0.05). The results of logistic regression analysis indicted that individual detection of serum carcinoembryonic antigens, CA199, CA242, CA125, and CA153 and their combined detection was effective for diagnosing colorectal cancer. Combined detection had a better diagnostic effect with a sensitivity of 94.2% and specificity of 97.7%; combining serum carcinoembryonic antigens, CA199, CA242, CA125, and CA153, with the support vector machine diagnosis model and back-propagation, a neural network diagnosis model was built with diagnostic accuracies of 82 and 75%, sensitivities of 85 and 80%, and specificities of 80 and 70%, respectively. Colorectal cancer diagnosis models based on the three machine learning algorithms showed high diagnostic value and can help obtain evidence for the early diagnosis of colorectal cancer.

  8. Foehn diagnosis and model comparison

    NASA Astrophysics Data System (ADS)

    Duerr, B.; Sprenger, M.; Fuhrer, O.; Burri, K.; Gutermann, T.; Hächler, P.; Neururer, A.; Richner, H.; Werner, R.

    2010-09-01

    The «Alpine Research Group Foehn Rhine Valley - Lake Constance (AGF)» (Arbeitsgemeinschaft Föhnforschung Rheintal-Bodensee) is analysing meteorological parameters and investigating foehn phenomena in the Rhine valley since 1971. Their main goal is to find plausible criteria for reliable foehn forecasts and to deepen our understanding of foehn in the target area. The presentation will focus on two main topics: (a) application of a fully automated foehn diagnosis tool; and (b) comparison of model data (COSMO-2, COSMO-7, VERA, INCA) with measurements in our target area. With respect to (a), the foehn diagnosis tool is applied for different meteorological stations of the Swiss meteorological network (SMN). Although it always uses the same six criteria (relative humidity, wind sector, wind force, gale maximum, difference of potential temperature to SMN site Gütsch and wind sector Gütsch), empirical thresholds have to be determined for each site individually. In doing so, foehn can be objectively detected and automatically identified as such in the most cases. In the second part, referring to (b), the foehn case of the 8th December 2006 is considered. It brought high wind velocities as well as a unusually far-reaching foehn, which was observed even north of Lake Constance. The synoptic- to local-scale dynamics of this foehn case is presented in detail, and the further developments within the framework of COSMO-2 to a high spatial resolution of 2.2 km facilitates the comparison of model forecasts with surface measurements. This comparison will be carried out with several parameters such as wind force, potential temperature and air pressure. Furthermore, the forecasted temporal evolution of foehn will be compared to the foehn's beginning and end detected by the foehn diagnosis tool.

  9. Two-stage damage diagnosis based on the distance between ARMA models and pre-whitening filters

    NASA Astrophysics Data System (ADS)

    Zheng, H.; Mita, A.

    2007-10-01

    This paper presents a two-stage damage diagnosis strategy for damage detection and localization. Auto-regressive moving-average (ARMA) models are fitted to time series of vibration signals recorded by sensors. In the first stage, a novel damage indicator, which is defined as the distance between ARMA models, is applied to damage detection. This stage can determine the existence of damage in the structure. Such an algorithm uses output only and does not require operator intervention. Therefore it can be embedded in the sensor board of a monitoring network. In the second stage, a pre-whitening filter is used to minimize the cross-correlation of multiple excitations. With this technique, the damage indicator can further identify the damage location and severity when the damage has been detected in the first stage. The proposed methodology is tested using simulation and experimental data. The analysis results clearly illustrate the feasibility of the proposed two-stage damage diagnosis methodology.

  10. System monitoring and diagnosis with qualitative models

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1991-01-01

    A substantial foundation of tools for model-based reasoning with incomplete knowledge was developed: QSIM (a qualitative simulation program) and its extensions for qualitative simulation; Q2, Q3 and their successors for quantitative reasoning on a qualitative framework; and the CC (component-connection) and QPC (Qualitative Process Theory) model compilers for building QSIM QDE (qualitative differential equation) models starting from different ontological assumptions. Other model-compilers for QDE's, e.g., using bond graphs or compartmental models, have been developed elsewhere. These model-building tools will support automatic construction of qualitative models from physical specifications, and further research into selection of appropriate modeling viewpoints. For monitoring and diagnosis, plausible hypotheses are unified against observations to strengthen or refute the predicted behaviors. In MIMIC (Model Integration via Mesh Interpolation Coefficients), multiple hypothesized models of the system are tracked in parallel in order to reduce the 'missing model' problem. Each model begins as a qualitative model, and is unified with a priori quantitative knowledge and with the stream of incoming observational data. When the model/data unification yields a contradiction, the model is refuted. When there is no contradiction, the predictions of the model are progressively strengthened, for use in procedure planning and differential diagnosis. Only under a qualitative level of description can a finite set of models guarantee the complete coverage necessary for this performance. The results of this research are presented in several publications. Abstracts of these published papers are presented along with abtracts of papers representing work that was synergistic with the NASA grant but funded otherwise. These 28 papers include but are not limited to: 'Combined qualitative and numerical simulation with Q3'; 'Comparative analysis and qualitative integral representations

  11. Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling.

    PubMed

    Li, Fan; Upadhyaya, Belle R; Coffey, Lonnie A

    2009-04-01

    This paper presents an incipient fault diagnosis approach based on the Group Method of Data Handling (GMDH) technique. The GMDH algorithm provides a generic framework for characterizing the interrelationships among a set of process variables of fossil power plant sub-systems and is employed to generate estimates of important variables in a data-driven fashion. In this paper, ridge regression techniques are incorporated into the ordinary least squares (OLS) estimator to solve regression coefficients at each layer of the GMDH network. The fault diagnosis method is applied to feedwater heater leak detection with data from an operating coal-fired plant. The results demonstrate the proposed method is capable of providing an early warning to operators when a process fault or an equipment fault occurs in a fossil power plant.

  12. A new mathematical model based on clinical and laboratory variables for the diagnosis of Sjögren's syndrome.

    PubMed

    Martin-Martin, L S; Latini, A; Pagano, A; Ragno, A; Stasi, R; Coppè, A; Davoli, G; Crescenzi, A; Alimonti, A; Migliore, A

    2003-05-01

    Sjögren's syndrome (SS) is a systemic autoimmune disease that mainly affects exocrine glands. A diagnosis of SS in its early stages has a potential clinical relevance, but it is difficult and cannot be made solely on clinical grounds. Several sets of diagnostic criteria have been proposed, but none has met with a general consensus. Minor salivary gland has been judged to be the "gold standard" for the diagnosis of SS. However, it is a painful procedure and has a small but significant proportion of both false positive and false negative results. The aim of our study was to develop a simple mathematical score that uses clinical and laboratory variables for diagnosing SS, thereby reducing the need of minor salivary gland. The following variables were included in the model: ANA, SS-A/SS-B, Schirmer's Test/BUT, C3/C4, serum gammaglobulin levels. One hundred consecutive individuals reporting clinical syndromes consistent with a sicca syndrome were included in the study. The application of our multifactorial mathematical model has shown a high predictive value for SS vs controls or vs patients with other autoimmune disorders (Sensitivity 93%, Specificity 100%), with an estimated minor salivary gland reduction of 77%. We conclude that our mathematical model can be considered a useful non-invasive approach for diagnosing Sjogren's Syndrome and recommend its validation on a larger scale.

  13. A theoretical model for the development of a diagnosis-based clinical decision rule for the management of patients with spinal pain

    PubMed Central

    Murphy, Donald R; Hurwitz, Eric L

    2007-01-01

    Background Spinal pain is a common problem, and disability related to spinal pain has great consequence in terms of human suffering, medical costs and costs to society. The traditional approach to the non-surgical management of patients with spinal pain, as well as to research in spinal pain, has been such that the type of treatment any given patient receives is determined more by what type of practitioner he or she sees, rather than by diagnosis. Furthermore, determination of treatment depends more on the type of practitioner than by the needs of the patient. Much needed is an approach to clinical management and research that allows clinicians to base treatment decisions on a reliable and valid diagnostic strategy leading to treatment choices that result in demonstrable outcomes in terms of pain relief and functional improvement. The challenges of diagnosis in patients with spinal pain, however, are that spinal pain is often multifactorial, the factors involved are wide ranging, and for most of these factors there exist no definitive objective tests. Discussion The theoretical model of a diagnosis-based clinical decision rule has been developed that may provide clinicians with an approach to non-surgical spine pain patients that allows for specific treatment decisions based on a specific diagnosis. This is not a classification scheme, but a thought process that attempts to identify most important features present in each individual patient. Presented here is a description of the proposed approach, in which reliable and valid assessment procedures are used to arrive at a working diagnosis which considers the disparate factors contributing to spinal pain. Treatment decisions are based on the diagnosis and the outcome of treatment can be measured. Summary In this paper, the theoretical model of a proposed diagnosis-based clinical decision rule is presented. In a subsequent manuscript, the current evidence for the approach will be systematically reviewed, and we will

  14. Developing a semantic web model for medical differential diagnosis recommendation.

    PubMed

    Mohammed, Osama; Benlamri, Rachid

    2014-10-01

    In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.

  15. A PC based fault diagnosis expert system

    NASA Technical Reports Server (NTRS)

    Marsh, Christopher A.

    1990-01-01

    The Integrated Status Assessment (ISA) prototype expert system performs system level fault diagnosis using rules and models created by the user. The ISA evolved from concepts to a stand-alone demonstration prototype using OPS5 on a LISP Machine. The LISP based prototype was rewritten in C and the C Language Integrated Production System (CLIPS) to run on a Personal Computer (PC) and a graphics workstation. The ISA prototype has been used to demonstrate fault diagnosis functions of Space Station Freedom's Operation Management System (OMS). This paper describes the development of the ISA prototype from early concepts to the current PC/workstation version used today and describes future areas of development for the prototype.

  16. Vibration-Based Damage Diagnosis in a Laboratory Cable-Stayed Bridge Model via an RCP-ARX Model Based Method

    NASA Astrophysics Data System (ADS)

    Michaelides, P. G.; Apostolellis, P. G.; Fassois, S. D.

    2011-07-01

    Vibration-based damage detection and identification in a laboratory cable-stayed bridge model is addressed under inherent, environmental, and experimental uncertainties. The problem is challenging as conventional stochastic methods face difficulties due to uncertainty underestimation. A novel method is formulated based on identified Random Coefficient Pooled ARX (RCP-ARX) representations of the dynamics and statistical hypothesis testing. The method benefits from the ability of RCP models in properly capturing uncertainty. Its effectiveness is demonstrated via a high number of experiments under a variety of damage scenarios.

  17. Theory-Based Diagnosis and Remediation of Writing Disabilities.

    ERIC Educational Resources Information Center

    Berninger, Virginia W.; And Others

    1991-01-01

    Briefly reviews recent trends in research on writing; introduces theory-based model being developed for differential diagnosis of writing disabilities at neuropsychological, linguistic, and cognitive levels; presents cases and patterns in cases that illustrate differential diagnosis of writing disabilities at linguistic level; and suggests…

  18. Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals

    NASA Astrophysics Data System (ADS)

    Silveira, Landulfo; Silveira, Fabrício Luiz; Bodanese, Benito; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu T.

    2012-07-01

    Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

  19. Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals.

    PubMed

    Silveira, Landulfo; Silveira, Fabrício Luiz; Bodanese, Benito; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu T

    2012-07-01

    Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

  20. Relative and Absolute Fit Evaluation in Cognitive Diagnosis Modeling

    ERIC Educational Resources Information Center

    Chen, Jinsong; de la Torre, Jimmy; Zhang, Zao

    2013-01-01

    As with any psychometric models, the validity of inferences from cognitive diagnosis models (CDMs) determines the extent to which these models can be useful. For inferences from CDMs to be valid, it is crucial that the fit of the model to the data is ascertained. Based on a simulation study, this study investigated the sensitivity of various fit…

  1. An architecture for the development of real-time fault diagnosis systems using model-based reasoning

    NASA Technical Reports Server (NTRS)

    Hall, Gardiner A.; Schuetzle, James; Lavallee, David; Gupta, Uday

    1992-01-01

    Presented here is an architecture for implementing real-time telemetry based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for offline entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed offline by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.

  2. Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: The impact of a local calibration

    PubMed Central

    2010-01-01

    Background In the financing of a national health system, where pharmaceutical spending is one of the main cost containment targets, predicting pharmacy costs for individuals and populations is essential for budget planning and care management. Although most efforts have focused on risk adjustment applying diagnostic data, the reliability of this information source has been questioned in the primary care setting. We sought to assess the usefulness of incorporating pharmacy data into claims-based predictive models (PMs). Developed primarily for the U.S. health care setting, a secondary objective was to evaluate the benefit of a local calibration in order to adapt the PMs to the Spanish health care system. Methods The population was drawn from patients within the primary care setting of Aragon, Spain (n = 84,152). Diagnostic, medication and prior cost data were used to develop PMs based on the Johns Hopkins ACG methodology. Model performance was assessed through r-squared statistics and predictive ratios. The capacity to identify future high-cost patients was examined through c-statistic, sensitivity and specificity parameters. Results The PMs based on pharmacy data had a higher capacity to predict future pharmacy expenses and to identify potential high-cost patients than the models based on diagnostic data alone and a capacity almost as high as that of the combined diagnosis-pharmacy-based PM. PMs provided considerably better predictions when calibrated to Spanish data. Conclusion Understandably, pharmacy spending is more predictable using pharmacy-based risk markers compared with diagnosis-based risk markers. Pharmacy-based PMs can assist plan administrators and medical directors in planning the health budget and identifying high-cost-risk patients amenable to care management programs. PMID:20092654

  3. Diagnosis of Parkinson’s disease on the basis of clinical–genetic classification: a population-based modelling study

    PubMed Central

    Nalls, Mike A.; McLean, Cory Y.; Rick, Jacqueline; Eberly, Shirley; Hutten, Samantha J.; Gwinn, Katrina; Sutherland, Margaret; Martinez, Maria; Heutink, Peter; Williams, Nigel; Hardy, John; Gasser, Thomas; Brice, Alexis; Price, T. Ryan; Nicolas, Aude; Keller, Margaux F.; Molony, Cliona; Gibbs, J. Raphael; Chen-Plotkin, Alice; Suh, Eunran; Letson, Christopher; Fiandaca, Massimo S.; Mapstone, Mark; Federoff, Howard J.; Noyce, Alastair J; Morris, Huw; Van Deerlin, Vivianna M.; Weintraub, Daniel; Zabetian, Cyrus; Hernandez, Dena G.; Lesage, Suzanne; Mullins, Meghan; Conley, Emily Drabant; Northover, Carrie; Frasier, Mark; Marek, Ken; Day-Williams, Aaron G.; Stone, David J.; Ioannidis, John P. A.; Singleton, Andrew B.

    2015-01-01

    Background Accurate diagnosis and early detection of complex disease has the potential to be of enormous benefit to clinical trialists, patients, and researchers alike. We sought to create a non-invasive, low-cost, and accurate classification model for diagnosing Parkinson’s disease risk to serve as a basis for future disease prediction studies in prospective longitudinal cohorts. Methods We developed a simple disease classifying model within 367 patients with Parkinson’s disease and phenotypically typical imaging data and 165 controls without neurological disease of the Parkinson’s Progression Marker Initiative (PPMI) study. Olfactory function, genetic risk, family history of PD, age and gender were algorithmically selected as significant contributors to our classifying model. This model was developed using the PPMI study then tested in 825 patients with Parkinson’s disease and 261 controls from five independent studies with varying recruitment strategies and designs including the Parkinson’s Disease Biomarkers Program (PDBP), Parkinson’s Associated Risk Study (PARS), 23andMe, Longitudinal and Biomarker Study in PD (LABS-PD), and Morris K. Udall Parkinson’s Disease Research Center of Excellence (Penn-Udall). Findings Our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0.923 (95% CI = 0.900 – 0.946) with high sensitivity (0.834, 95% CI = 0.711 – 0.883) and specificity (0.903, 95% CI = 0.824 – 0.946) in PPMI at its optimal AUC threshold (0.655). The model is also well-calibrated with all Hosmer-Lemeshow simulations suggesting that when parsed into random subgroups, the actual data mirrors that of the larger expected data, demonstrating that our model is robust and fits well. Likewise external validation shows excellent classification of PD with AUCs of 0.894 in PDBP, 0.998 in PARS, 0.955 in 23andMe, 0.929 in LABS-PD, and 0.939 in Penn-Udall. Additionally, when our model

  4. An observer based approach for achieving fault diagnosis and fault tolerant control of systems modeled as hybrid Petri nets.

    PubMed

    Renganathan, K; Bhaskar, VidhyaCharan

    2011-07-01

    In this paper, we propose an approach for achieving detection and identification of faults, and provide fault tolerant control for systems that are modeled using timed hybrid Petri nets. For this purpose, an observer based technique is adopted which is useful in detection of faults, such as sensor faults, actuator faults, signal conditioning faults, etc. The concepts of estimation, reachability and diagnosability have been considered for analyzing faulty behaviors, and based on the detected faults, different schemes are proposed for achieving fault tolerant control using optimization techniques. These concepts are applied to a typical three tank system and numerical results are obtained.

  5. Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: a Monte-Carlo-model-based approach

    PubMed Central

    Zhu, Changfang; Palmer, Gregory M.; Breslin, Tara M.; Harter, Josephine; Ramanujam, Nirmala

    2009-01-01

    We explore the use of Monte-Carlo-model-based approaches for the analysis of fluorescence and diffuse reflectance spectra measured ex vivo from breast tissues. These models are used to extract the absorption, scattering, and fluorescence properties of malignant and nonmalignant tissues and to diagnose breast cancer based on these intrinsic tissue properties. Absorption and scattering properties, including β-carotene concentration, total hemoglobin concentration, hemoglobin saturation, and the mean reduced scattering coefficient are derived from diffuse reflectance spectra using a previously developed Monte Carlo model of diffuse reflectance. A Monte Carlo model of fluorescence described in an earlier manuscript was employed to retrieve the intrinsic fluorescence spectra. The intrinsic fluorescence spectra were decomposed into several contributing components, which we attribute to endogenous fluorophores that may present in breast tissues including collagen, NADH, and retinol/vitamin A. The model-based approaches removes any dependency on the instrument and probe geometry. The relative fluorescence contributions of individual fluorescing components, as well as β-carotene concentration, hemoglobin saturation, and the mean reduced scattering coefficient display statistically significant differences between malignant and adipose breast tissues. The hemoglobin saturation and the reduced scattering coefficient display statistically significant differences between malignant and fibrous/benign breast tissues. A linear support vector machine classification using (1) fluorescence properties alone, (2) absorption and scattering properties alone, and (3) the combination of all tissue properties achieves comparable classification accuracies of 81 to 84% in sensitivity and 75 to 89% in specificity for discriminating malignant from nonmalignant breast tissues, suggesting each set of tissue properties are diagnostically useful for the discrimination of breast malignancy. PMID

  6. Application of the fault diagnosis strategy based on hierarchical information fusion in motors fault diagnosis

    NASA Astrophysics Data System (ADS)

    Xia, Li; Fei, Qi

    2006-03-01

    This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hierarchical information fusion fault diagnosis strategy by combining the neural network technique and the fused decision diagnosis based on D-S Theory, and established a corresponding functional model. Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory. The application of the strategy to diagnosing faults of motor bearings has proved that this method is of fairly high accuracy and reliability in fault diagnosis.

  7. A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis

    PubMed Central

    Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W.

    2013-01-01

    The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully. PMID:24253191

  8. A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

    2015-01-01

    Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

  9. Hierarchical Item Response Models for Cognitive Diagnosis

    ERIC Educational Resources Information Center

    Hansen, Mark Patrick

    2013-01-01

    Cognitive diagnosis models (see, e.g., Rupp, Templin, & Henson, 2010) have received increasing attention within educational and psychological measurement. The popularity of these models may be largely due to their perceived ability to provide useful information concerning both examinees (classifying them according to their attribute profiles)…

  10. Image-based micro-finite-element modeling for improved distal radius strength diagnosis: moving from bench to bedside.

    PubMed

    Pistoia, W; van Rietbergen, B; Lochmüller, E-M; Lill, C A; Eckstein, F; Rüegsegger, P

    2004-01-01

    Although osteoporosis is characterized by quantitative (mass) and qualitative (structural) changes, standard clinical techniques (dual-energy X-ray absorptiometry, DXA) only measure the former. Three-dimensional micro-finite-element (micro-FE) models based on high-resolution images can account for structural aspects as well, and it has recently been shown that an improved prediction of distal radius strength is possible with micro-FE analysis. A clinical application of this technique, however, is limited by its high imaging and computational demands. The objective of this study is to investigate if an improved prediction of bone strength can be obtained as well when only a small part of the radius is used for micro-FE modeling. Images of a 1-cm region of the metaphysis of the distal radius of 54 cadaver arms (mean age: 82 +/- 9 SD) made with a three-dimensional peripheral quantitative computed tomography (pQCT) device at 165- micro m resolution formed the basis for micro-FE models that were used to predict the bone failure load. Following imaging, specimens were experimentally compressed to failure to produce a Colles'-type fracture. Failure loads predicted from micro-FE analyses agreed well with those measured experimentally (R2 = 0.66, p < 0.001). Lower correlations were observed with bone mass (R2 = 0.48, p < 0.001) and microstructural parameters (R2 = 0.47, p < 0.001). Hence, even when only a small region is modeled, micro-FE analysis provides an improved prediction of radius strength.

  11. Calibrating perceived understanding and competency in probability concepts: A diagnosis of learning difficulties based on Rasch probabilistic model

    NASA Astrophysics Data System (ADS)

    Mahmud, Zamalia; Porter, Anne; Salikin, Masniyati; Ghani, Nor Azura Md

    2015-12-01

    Students' understanding of probability concepts have been investigated from various different perspectives. Competency on the other hand is often measured separately in the form of test structure. This study was set out to show that perceived understanding and competency can be calibrated and assessed together using Rasch measurement tools. Forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW have volunteered to participate in the study. Rasch measurement which is based on a probabilistic model is used to calibrate the responses from two survey instruments and investigate the interactions between them. Data were captured from the e-learning platform Moodle where students provided their responses through an online quiz. The study shows that majority of the students perceived little understanding about conditional and independent events prior to learning about it but tend to demonstrate a slightly higher competency level afterward. Based on the Rasch map, there is indication of some increase in learning and knowledge about some probability concepts at the end of the two weeks lessons on probability concepts.

  12. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, L.J.; Keller, P.E.

    1997-10-28

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

  13. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  14. Failure diagnosis using discrete event models

    SciTech Connect

    Sampath, M.; Sengupta, R.; Lafortune, S.; Teneketzis, D.; Sinnamohideen, K.

    1994-12-31

    We propose a Discrete Event Systems (DES) approach to the failure diagnosis problem. We present a methodology for modeling physical systems in a DES framework. We discuss the notion of diagnosability and present the construction procedure of the diagnoser. Finally, we illustrate our approach using a Heating, Ventilation and Air Conditioning (HVAC) system.

  15. The Concept of Sasang Health Index and Constitution-Based Health Assessment: An Integrative Model with Computerized Four Diagnosis Methods

    PubMed Central

    Kim, Jaeuk U.; Ku, Boncho; Kim, Young-Min; Do, Jun-Hyeong; Jang, Eunsu; Jeon, Young Ju; Kim, Keun Ho; Kim, Jong Yeol

    2013-01-01

    Sasang constitutional medicine (SCM) shares its philosophy with that of personalized medicine: it provides constitution-specific treatment and healthcare individualized for each patient. In this work, we propose the concept of the Sasang Health Index (SHI) as an attempt to assess the individualized health status in the framework of SCM. From the target population of females in their fifties and older, we recruited 298 subjects and collected their physiological data, including complexion, radial pulse, and voice, and their questionnaire responses. The health status of each subject was evaluated by two Korean medical doctors independently, and the SHI model was obtained by combining all the integrative features of the phenotype data using a regression technique. As a result, most subjects belonged to either the healthy, subhealthy, or slightly diseased group, and the intraclass correlation coefficient between the two doctors' health scoring reached 0.95. We obtained an SHI model for each constitution type with adjusted R-squares of 0.50, 0.56, and 0.30, for the TE, SE, and SY constitution types, respectively. In the proposed SHI model, the significant characteristics used in the health assessment consisted of constitution-specific features in accordance with the classic literature and features common to all the constitution types. PMID:23843888

  16. Evaluation of Model Fit in Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Hu, Jinxiang; Miller, M. David; Huggins-Manley, Anne Corinne; Chen, Yi-Hsin

    2016-01-01

    Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC),…

  17. Crystallographic Model Validation: from Diagnosis to Healing

    PubMed Central

    Richardson, Jane S.; Prisant, Michael G.; Richardson, David C.

    2013-01-01

    Model validation has evolved from a passive final gatekeeping step to an ongoing diagnosis and healing process that enables significant improvement of accuracy. A recent phase of active development was spurred by the worldwide Protein Data Bank requiring data deposition and establishing Validation Task Force committees, by strong growth in high-quality reference data, by new speed and ease of computations, and by an upswing of interest in large molecular machines and structural ensembles. Progress includes automated correction methods, concise and user-friendly validation reports for referees and on the PDB websites, extension of error correction to RNA and error diagnosis to ligands, carbohydrates, and membrane proteins, and a good start on better methods for low resolution and for multiple conformations. PMID:24064406

  18. Fitting Data to Model: Structural Equation Modeling Diagnosis Using Two Scatter Plots

    ERIC Educational Resources Information Center

    Yuan, Ke-Hai; Hayashi, Kentaro

    2010-01-01

    This article introduces two simple scatter plots for model diagnosis in structural equation modeling. One plot contrasts a residual-based M-distance of the structural model with the M-distance for the factor score. It contains information on outliers, good leverage observations, bad leverage observations, and normal cases. The other plot contrasts…

  19. Crack Diagnosis of Wind Turbine Blades Based on EMD Method

    NASA Astrophysics Data System (ADS)

    Hong-yu, CUI; Ning, DING; Ming, HONG

    2016-11-01

    Wind turbine blades are both the source of power and the core technology of wind generators. After long periods of time or in some extreme conditions, cracks or damage can occur on the surface of the blades. If the wind generators continue to work at this time, the crack will expand until the blade breaks, which can lead to incalculable losses. Therefore, a crack diagnosis method based on EMD for wind turbine blades is proposed in this paper. Based on aerodynamics and fluid-structure coupling theory, an aero-elastic analysis on wind turbine blades model is first made in ANSYS Workbench. Second, based on the aero-elastic analysis and EMD method, the blade cracks are diagnosed and identified in the time and frequency domains, respectively. Finally, the blade model, strain gauge, dynamic signal acquisition and other equipment are used in an experimental study of the aero-elastic analysis and crack damage diagnosis of wind turbine blades to verify the crack diagnosis method proposed in this paper.

  20. Sensor-based fault diagnosis in a flight expert system

    NASA Technical Reports Server (NTRS)

    Ali, M.; Scharnhorst, D. A.

    1985-01-01

    A prototype of a knowledge-based flight expert system (FLES) has been developed to assist airplane pilots in monitoring, analyzing, and diagnosing faults and to provide support in reducing the pilot's own mistakes. A sensor simulation model has been developed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Advanced Concept System (ACS), a future-generation airplane, and partly on the Boeing 737, an existing airplane. The architecture of FLES contains several subsystems. One of the major subsystems performs fault diagnosis in the electrical system of the ACS. This paper describes the mechanism and functionality of the automatic diagnosis performed in this expert system.

  1. Wireless laptop-based phonocardiograph and diagnosis

    PubMed Central

    2015-01-01

    Auscultation is used to evaluate heart health, and can indicate when it’s needed to refer a patient to a cardiologist. Advanced phonocardiograph (PCG) signal processing algorithms are developed to assist the physician in the initial diagnosis but they are primarily designed and demonstrated with research quality equipment. Therefore, there is a need to demonstrate the applicability of those techniques with consumer grade instrument. Furthermore, routine monitoring would benefit from a wireless PCG sensor that allows continuous monitoring of cardiac signals of patients in physical activity, e.g., treadmill or weight exercise. In this work, a low-cost portable and wireless healthcare monitoring system based on PCG signal is implemented to validate and evaluate the most advanced algorithms. Off-the-shelf electronics and a notebook PC are used with MATLAB codes to record and analyze PCG signals which are collected with a notebook computer in tethered and wireless mode. Physiological parameters based on the S1 and S2 signals and MATLAB codes are demonstrated. While the prototype is based on MATLAB, the later is not an absolute requirement. PMID:26339555

  2. Wireless laptop-based phonocardiograph and diagnosis.

    PubMed

    Dao, Amy T

    2015-01-01

    Auscultation is used to evaluate heart health, and can indicate when it's needed to refer a patient to a cardiologist. Advanced phonocardiograph (PCG) signal processing algorithms are developed to assist the physician in the initial diagnosis but they are primarily designed and demonstrated with research quality equipment. Therefore, there is a need to demonstrate the applicability of those techniques with consumer grade instrument. Furthermore, routine monitoring would benefit from a wireless PCG sensor that allows continuous monitoring of cardiac signals of patients in physical activity, e.g., treadmill or weight exercise. In this work, a low-cost portable and wireless healthcare monitoring system based on PCG signal is implemented to validate and evaluate the most advanced algorithms. Off-the-shelf electronics and a notebook PC are used with MATLAB codes to record and analyze PCG signals which are collected with a notebook computer in tethered and wireless mode. Physiological parameters based on the S1 and S2 signals and MATLAB codes are demonstrated. While the prototype is based on MATLAB, the later is not an absolute requirement.

  3. Measurement of Psychological Disorders Using Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Templin, Jonathan L.; Henson, Robert A.

    2006-01-01

    Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article…

  4. Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

    NASA Technical Reports Server (NTRS)

    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

  5. Teaching Evidence-Based Physical Diagnosis: Six Bedside Lessons.

    PubMed

    McGee, Steven

    2016-12-01

    Evidence-based physical diagnosis is an essential part of the bedside curriculum. By using the likelihood ratio, a simple measure of diagnostic accuracy, teachers can quickly adapt this approach to their bedside teaching. Six recurring themes in evidence-based physical diagnosis are fully reviewed, with examples, in this article.

  6. Computer modeling of lung cancer diagnosis-to-treatment process.

    PubMed

    Ju, Feng; Lee, Hyo Kyung; Osarogiagbon, Raymond U; Yu, Xinhua; Faris, Nick; Li, Jingshan

    2015-08-01

    We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

  7. Computer modeling of lung cancer diagnosis-to-treatment process

    PubMed Central

    Ju, Feng; Lee, Hyo Kyung; Osarogiagbon, Raymond U.; Yu, Xinhua; Faris, Nick

    2015-01-01

    We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed. PMID:26380181

  8. Multivariate Predictive Model for Dyslexia Diagnosis

    ERIC Educational Resources Information Center

    Le Jan, Guylaine; Le Bouquin-Jeannes, Regine; Costet, Nathalie; Troles, Nolwenn; Scalart, Pascal; Pichancourt, Dominique; Faucon, Gerard; Gombert, Jean-Emile

    2011-01-01

    Dyslexia is a specific disorder of language development that mainly affects reading. Etiological researches have led to multiple hypotheses which induced various diagnosis methods and rehabilitation treatments so that many different tests are used by practitioners to identify dyslexia symptoms. Our purpose is to determine a subset of the most…

  9. Knowledge-based fault diagnosis system for refuse collection vehicle

    NASA Astrophysics Data System (ADS)

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-05-01

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  10. Knowledge-based fault diagnosis system for refuse collection vehicle

    SciTech Connect

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-05-15

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  11. Diagnosis of Photochemical Ozone Production Rates and Limiting Factors based on Observation-based Modeling Approach over East Asia: Impact of Radical Chemistry Mechanism and Ozone-Control Implications

    NASA Astrophysics Data System (ADS)

    Kanaya, Y.

    2015-12-01

    Growth of tropospheric ozone, causing health and climate impacts, is concerned over East Asia, because emissions of precursors have dramatically increased. Photochemical production rates of ozone and limiting factors, primarily studied for urban locations, have been poorly assessed within a perspective of regional-scale air pollution over East Asia. We performed comprehensive observations of ozone precursors at several locations with regional representativeness and made such assessment based on the observation-based modeling approach. Here, diagnosis at Fukue Island (32.75°N, 128.68°E) remotely located in western Japan (May 2009) is highlighted, where the highest 10% of hourly ozone concentrations reached 72‒118 ppb during May influenced by Asian continental outflow. The average in-situ ozone production rate was estimated to be 6.8 ppb per day, suggesting that in-travel production was still active, while larger buildup must have occurred beforehand. Information on the chemical status of the air mass arriving in Japan is important, because it affects how further ozone production occurs after precursor addition from Japanese domestic emissions. The main limiting factor of ozone production was usually NOx, suggesting that domestic NOx emission control is important in reducing further ozone production and the incidence of warning issuance (>120 ppb). VOCs also increased the ozone production rate, and occasionally (14% of time) became dominant. This analysis implies that the VOC reduction legislation recently enacted should be effective. The uncertainty in the radical chemistry mechanism governing ozone production had a non-negligible impact, but the main conclusion relevant to policy was not altered. When chain termination was augmented by HO2-H2O + NO/NO2 reactions and by heterogeneous loss of HO2 on aerosol particle surfaces, the daily ozone production rate decreased by <24%, and the fraction of hours when the VOC-limited condition occurred varied from 14% to 13

  12. Analog filter diagnosis using the oscillation based method

    NASA Astrophysics Data System (ADS)

    Andrejević Stošović, Miona; Milić, Miljana; Litovski, Vanćo

    2012-12-01

    Oscillation Based Testing (OBT) is an effective and simple solution to the testing problem of continuous time analogue electronic filters. In this paper, diagnosis based on OBT is described for the first time. It will be referred to as OBD. A fault dictionary is created and used to perform diagnosis with artificial neural networks (ANNs) implemented as classifiers. The robustness of the ANN diagnostic concept is also demonstrated by the addition of white noise to the “measured” signals. The implementation of the new concept is demonstrated by testing and diagnosis of a second order notch cell realized with one operational amplifier. Single soft and catastrophic faults are considered in detail and an example of the diagnosis of double soft faults is also given.

  13. Higher-Order Latent Trait Models for Cognitive Diagnosis

    ERIC Educational Resources Information Center

    de la Torre, Jimmy; Douglas, Jeffrey A.

    2004-01-01

    Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative…

  14. Measurement of psychological disorders using cognitive diagnosis models.

    PubMed

    Templin, Jonathan L; Henson, Robert A

    2006-09-01

    Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article presents the development of a new cognitive diagnosis model for use in psychological assessment--the DINO (deterministic input; noisy "or" gate) model--which, as an illustrative example, is applied to evaluate and diagnose pathological gamblers. As part of this example, a demonstration of the estimates obtained by cognitive diagnosis models is provided. Such estimates include the probability an individual meets each of a set of dichotomous Diagnostic and Statistical Manual of Mental Disorders (text revision [DSM-IV-TR]; American Psychiatric Association, 2000) criteria, resulting in an estimate of the probability an individual meets the DSM-IV-TR definition for being a pathological gambler. Furthermore, a demonstration of how the hypothesized underlying factors contributing to pathological gambling can be measured with the DINO model is presented, through use of a covariance structure model for the tetrachoric correlation matrix of the dichotomous latent variables representing DSM-IV-TR criteria.

  15. An Event-Based Approach to Distributed Diagnosis of Continuous Systems

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

    2010-01-01

    Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

  16. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

    PubMed

    Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

    2016-12-22

    Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

  17. Pattern-based fault diagnosis using neural networks

    NASA Technical Reports Server (NTRS)

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

    1988-01-01

    An architecture for a real-time pattern-based diagnostic expert system capable of accommodating noisy, incomplete, and possibly erroneous input data is outlined. Results from prototype systems applied to jet and rocket engine fault diagnosis are presented. The ability of a neural network-based system to be trained via the presentation of behavioral patterns associated with fault conditions is demonstrated.

  18. Skin image illumination modeling and chromophore identification for melanoma diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Zhao; Zerubia, Josiane

    2015-05-01

    The presence of illumination variation in dermatological images has a negative impact on the automatic detection and analysis of cutaneous lesions. This paper proposes a new illumination modeling and chromophore identification method to correct lighting variation in skin lesion images, as well as to extract melanin and hemoglobin concentrations of human skin, based on an adaptive bilateral decomposition and a weighted polynomial curve fitting, with the knowledge of a multi-layered skin model. Different from state-of-the-art approaches based on the Lambert law, the proposed method, considering both specular reflection and diffuse reflection of the skin, enables us to address highlight and strong shading effects usually existing in skin color images captured in an uncontrolled environment. The derived melanin and hemoglobin indices, directly relating to the pathological tissue conditions, tend to be less influenced by external imaging factors and are more efficient in describing pigmentation distributions. Experiments show that the proposed method gave better visual results and superior lesion segmentation, when compared to two other illumination correction algorithms, both designed specifically for dermatological images. For computer-aided diagnosis of melanoma, sensitivity achieves 85.52% when using our chromophore descriptors, which is 8~20% higher than those derived from other color descriptors. This demonstrates the benefit of the proposed method for automatic skin disease analysis.

  19. Epiretinal membrane: optical coherence tomography-based diagnosis and classification

    PubMed Central

    Stevenson, William; Prospero Ponce, Claudia M; Agarwal, Daniel R; Gelman, Rachel; Christoforidis, John B

    2016-01-01

    Epiretinal membrane (ERM) is a disorder of the vitreomacular interface characterized by symptoms of decreased visual acuity and metamorphopsia. The diagnosis and classification of ERM has traditionally been based on clinical examination findings. However, modern optical coherence tomography (OCT) has proven to be more sensitive than clinical examination for the diagnosis of ERM. Furthermore, OCT-derived findings, such as central foveal thickness and inner segment ellipsoid band integrity, have shown clinical relevance in the setting of ERM. To date, no OCT-based ERM classification scheme has been widely accepted for use in clinical practice and investigation. Herein, we review the pathogenesis, diagnosis, and classification of ERMs and propose an OCT-based ERM classification system. PMID:27099458

  20. Epiretinal membrane: optical coherence tomography-based diagnosis and classification.

    PubMed

    Stevenson, William; Prospero Ponce, Claudia M; Agarwal, Daniel R; Gelman, Rachel; Christoforidis, John B

    2016-01-01

    Epiretinal membrane (ERM) is a disorder of the vitreomacular interface characterized by symptoms of decreased visual acuity and metamorphopsia. The diagnosis and classification of ERM has traditionally been based on clinical examination findings. However, modern optical coherence tomography (OCT) has proven to be more sensitive than clinical examination for the diagnosis of ERM. Furthermore, OCT-derived findings, such as central foveal thickness and inner segment ellipsoid band integrity, have shown clinical relevance in the setting of ERM. To date, no OCT-based ERM classification scheme has been widely accepted for use in clinical practice and investigation. Herein, we review the pathogenesis, diagnosis, and classification of ERMs and propose an OCT-based ERM classification system.

  1. Probability-Based Inference in Cognitive Diagnosis

    DTIC Science & Technology

    1994-02-01

    of variables in the student model. In Siegler’s study , this corresponds to determining how a child with a given set of strategies at her disposal...programs are commercially available to carry out the number-crunching aspect. We used Andersen, Jensen, Olesen, and Jensen’s (1989) HUGIN program and Noetic ... studying how they are typically acquired (e.g., in mechanics, Clement, 1982; in ratio and proportional reasoning, Karplus, Pulos, & Stage, 1983), and

  2. Efficient diagnosis of multiprocessor systems under probabilistic models

    NASA Technical Reports Server (NTRS)

    Blough, Douglas M.; Sullivan, Gregory F.; Masson, Gerald M.

    1989-01-01

    The problem of fault diagnosis in multiprocessor systems is considered under a probabilistic fault model. The focus is on minimizing the number of tests that must be conducted in order to correctly diagnose the state of every processor in the system with high probability. A diagnosis algorithm that can correctly diagnose the state of every processor with probability approaching one in a class of systems performing slightly greater than a linear number of tests is presented. A nearly matching lower bound on the number of tests required to achieve correct diagnosis in arbitrary systems is also proven. Lower and upper bounds on the number of tests required for regular systems are also presented. A class of regular systems which includes hypercubes is shown to be correctly diagnosable with high probability. In all cases, the number of tests required under this probabilistic model is shown to be significantly less than under a bounded-size fault set model. Because the number of tests that must be conducted is a measure of the diagnosis overhead, these results represent a dramatic improvement in the performance of system-level diagnosis techniques.

  3. TU-G-204-06: Correlation Between Texture Analysis-Based Model Observer and Human Observer in Diagnosis of Ischemic Infarct in Non-Contrast Head CT of Adults

    SciTech Connect

    Li, B; Fujita, A; Buch, K; Sakai, O

    2015-06-15

    Purpose: To investigate the correlation between texture analysis-based model observer and human observer in the task of diagnosis of ischemic infarct in non-contrast head CT of adults. Methods: Non-contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro-reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro -radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9-point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro-radiologist placed multiple ROIs over the infarcts. These ROIs were entered into a texture analysis software package. Forty-two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2-D features, were computed and averaged over the images per dataset. The Fisher-coefficient (ratio of between-class variance to in-class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher -coefficient were entered into linear multivariate regression for iterative modeling. Results: Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p-value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R-sq=0.77). The root-mean-square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased. Conclusion: For diagnosis of ischemic infarct in non-contrast head CT in adults, the predicted image quality scores from texture analysis-based model observer was highly correlated with that of human observers for various noise levels. Texture-based model observer can characterize image quality of low contrast

  4. Design and Implementation of Harmful Algal Bloom Diagnosis System Based on J2EE Platform

    NASA Astrophysics Data System (ADS)

    Guo, Chunfeng; Zheng, Haiyong; Ji, Guangrong; Lv, Liang

    According to the shortcomings which are time consuming and laborious of the traditional HAB (Harmful Algal Bloom) diagnosis by the experienced experts using microscope, all kinds of methods and technologies to identify HAB emerged such as microscopic images, molecular biology, characteristics of pigments analysis, fluorescence spectra, inherent optical properties, etc. This paper proposes the design and implementation of a web-based diagnosis system integrating the popular methods for HAB identification. This system is designed with J2EE platform based on MVC (Model-View-Controller) model as well as technologies such as JSP, Servlets, EJB and JDBC.

  5. Object-oriented fault tree models applied to system diagnosis

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; Patterson-Hine, F. A.

    1990-01-01

    When a diagnosis system is used in a dynamic environment, such as the distributed computer system planned for use on Space Station Freedom, it must execute quickly and its knowledge base must be easily updated. Representing system knowledge as object-oriented augmented fault trees provides both features. The diagnosis system described here is based on the failure cause identification process of the diagnostic system described by Narayanan and Viswanadham. Their system has been enhanced in this implementation by replacing the knowledge base of if-then rules with an object-oriented fault tree representation. This allows the system to perform its task much faster and facilitates dynamic updating of the knowledge base in a changing diagnosis environment. Accessing the information contained in the objects is more efficient than performing a lookup operation on an indexed rule base. Additionally, the object-oriented fault trees can be easily updated to represent current system status. This paper describes the fault tree representation, the diagnosis algorithm extensions, and an example application of this system. Comparisons are made between the object-oriented fault tree knowledge structure solution and one implementation of a rule-based solution. Plans for future work on this system are also discussed.

  6. Chip-Based Sensors for Disease Diagnosis

    NASA Astrophysics Data System (ADS)

    Fang, Zhichao

    Nucleic acid analysis is one of the most important disease diagnostic approaches in medical practice, and has been commonly used in cancer biomarker detection, bacterial speciation and many other fields in laboratory. Currently, the application of powerful research methods for genetic analysis, including the polymerase chain reaction (PCR), DNA sequencing, and gene expression profiling using fluorescence microarrays, are not widely used in hospitals and extended-care units due to high-cost, long detection times, and extensive sample preparation. Bioassays, especially chip-based electrochemical sensors, may be suitable for the next generation of rapid, sensitive, and multiplexed detection tools. Herein, we report three different microelectrode platforms with capabilities enabled by nano- and microtechnology: nanoelectrode ensembles (NEEs), nanostructured microelectrodes (NMEs), and hierarchical nanostructured microelectrodes (HNMEs), all of which are able to directly detect unpurified RNA in clinical samples without enzymatic amplification. Biomarkers that are cancer and infectious disease relevant to clinical medicine were chosen to be the targets. Markers were successfully detected with clinically-relevant sensitivity. Using peptide nucleic acids (PNAs) as probes and an electrocatalytic reporter system, NEEs were able to detect prostate cancer-related gene fusions in tumor tissue samples with 100 ng of RNA. The development of NMEs improved the sensitivity of the assay further to 10 aM of DNA target, and multiplexed detection of RNA sequences of different prostate cancer-related gene fusion types was achieved on the chip-based NMEs platform. An HNMEs chip integrated with a bacterial lysis device was able to detect as few as 25 cfu bacteria in 30 minutes and monitor the detection in real time. Bacterial detection could also be performed in neat urine samples. The development of these versatile clinical diagnostic tools could be extended to the detection of various

  7. Fitting data to model: structural equation modeling diagnosis using two scatter plots.

    PubMed

    Yuan, Ke-Hai; Hayashi, Kentaro

    2010-12-01

    This article introduces two simple scatter plots for model diagnosis in structural equation modeling. One plot contrasts a residual-based M-distance of the structural model with the M-distance for the factor score. It contains information on outliers, good leverage observations, bad leverage observations, and normal cases. The other plot contrasts the residual-based M-distance with the quantile of a chi distribution. It allows the researcher to visually identify clusters of potential outliers. The article further studies the effect of the potential outliers on the overall model evaluation when they are removed according to the order of the clusters exhibited in the plot. Suggestions are provided on determining the outlier status of outstanding cases in real data analysis. Recommendations are also made on the choice of robust methods and maximum likelihood following outlier removal.

  8. Vibration signal models for fault diagnosis of planet bearings

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.

    2016-05-01

    Rolling element bearings are key components of planetary gearboxes. Among them, the motion of planet bearings is very complex, encompassing spinning and revolution. Therefore, planet bearing vibrations are highly intricate and their fault characteristics are completely different from those of fixed-axis case, making planet bearing fault diagnosis a difficult topic. In order to address this issue, we derive the explicit equations for calculating the characteristic frequency of outer race, rolling element and inner race fault, considering the complex motion of planet bearings. We also develop the planet bearing vibration signal model for each fault case, considering the modulation effects of load zone passing, time-varying angle between the gear pair mesh and fault induced impact force, as well as the time-varying vibration transfer path. Based on the developed signal models, we derive the explicit equations of Fourier spectrum in each fault case, and summarize the vibration spectral characteristics respectively. The theoretical derivations are illustrated by numerical simulation, and further validated experimentally and all the three fault cases (i.e. outer race, rolling element and inner race localized fault) are diagnosed.

  9. Self-calibrating models for dynamic monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1996-01-01

    A method for automatically building qualitative and semi-quantitative models of dynamic systems, and using them for monitoring and fault diagnosis, is developed and demonstrated. The qualitative approach and semi-quantitative method are applied to monitoring observation streams, and to design of non-linear control systems.

  10. Pattern-Based Medical Diagnosis on a Microcomputer*

    PubMed Central

    Fisher, Paul R.; Kurlander, David J.

    1980-01-01

    A differential diagnosis microcomputer program has been written that utilizes both pattern-recognition and logical analysis in its algorithm. Together with auxilliary routines, the program (called DX) performs medical diagnosis, stores and retrieves patient information, creates new model symptom sets using information from the patient pool, and trains its own data matrices. Designed to be user oriented, DX can communicate the reasoning behind its decisions, thereby complementing the physician's own skills. This microcomputer system is geared towards community practices because the matrices are trained by statistics DX gathers from the local patient population. Although the program was written to be used in any area of medical practice, the arrays are presently being trained for the diagnosis of presentations of the acute abdomen.

  11. Polycrystalline diamond based detector for Z-pinch plasma diagnosis.

    PubMed

    Liu, Linyue; Ouyang, Xiaoping; Zhao, Jizhen; Chen, Liang; Wang, Lan

    2010-08-01

    A detector setup based on polycrystalline chemical-vapor-deposition diamond film is developed with great characteristics: low dark current (lower than 60 pA within 3 V/mum), fast pulsed response time (rise time: 2-3 ns), flat spectral response (3-5 keV), easy acquisition, low cost, and relative large sensitive area. The characterizing data on Qiangguang-I accelerator show that this detector can satisfy the practical requirements in Z-pinch plasma diagnosis very well, which offers a promising prototype for the x-ray detection in Z-pinch diagnosis.

  12. Diagnosis of helicopter gearboxes using structure-based networks

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

    1995-01-01

    A connectionist network is introduced for fault diagnosis of helicopter gearboxes that incorporates knowledge of the gearbox structure and characteristics of the vibration features as its fuzzy weights. Diagnosis is performed by propagating the abnormal features of vibration measurements through this Structure-Based Connectionist Network (SBCN), the outputs of which represent the fault possibility values for individual components of the gearbox. The performance of this network is evaluated by applying it to experimental vibration data from an OH-58A helicopter gearbox. The diagnostic results indicate that the network performance is comparable to those obtained from supervised pattern classification.

  13. Breast cancer early diagnosis based on hybrid strategy.

    PubMed

    Li, Peng; Bi, Tingting; Huang, Jiuling; Li, Siben

    2014-01-01

    The frequent occurrence of breast cancer and its serious consequences have attracted worldwide attention in recent years. Problems such as low rate of accuracy and poor self-adaptability still exist in traditional diagnosis. In order to solve these problems, an AdaBoost-SVM classification algorithm, combined with the cluster boundary sampling preprocessing techniques (CBS-AdaBoost-SVM), is proposed in this paper for the early diagnosis of breast cancer. The algorithm uses machine learning method to diagnose the unknown image data. Moreover, not all of the characteristics play positive roles for classification. To address this issue the paper delete redundant features by using Rough set attribute reduction algorithm based on the genetic algorithm (GA). The effectiveness of the proposed methods are examined on DDSM by calculating its accuracy, confusion matrix, and receiver operating characteristic curves, which give important clues to the physicians for early diagnosis of breast cancer.

  14. Model-supported diagnosis for integrated vehicle health management of space systems

    NASA Astrophysics Data System (ADS)

    Dannenmann, Peter; Busch, Wolfgang

    2003-08-01

    In this paper we present a new architecture for integrating system health monitoring tasks into the development- and life cycle of space systems. On the basis of model-supported diagnosis technology the presented method uses information for diagnosis purposes that is already gathered during the development of a technical system. This information is extracted from simulation models used for design-studies and what-if-analyses during the design- and development phase. For building up these simulation models easily, we developed a library of generic models of spacecraft components. These models cover the components' nominal and off-nominal behavior as it is specified in the component FMECAs. By combining and parametrizing the components a system model is built up. Since due to the limited resources on board of a spacecraft we can not use the model directly for model-based diagnosis, we use a model-supported approach: By systematically simulating possible component faults within the system's operational modes, we retrieve a set of measurement data that serve as symptoms to the failure modes. By classifying these data we get a knowledge-base for a symptom-based on-board diagnosis system. In order to cope with the uncertainty in the measurement data, this diagnosis system has been realized as a fuzzy system that on the basis of the given knowledge-base computes the most probable diagnoses from the given symptoms. The described system has been implemented within Astrium's Columbus Simulation System (CSS) and has been evaluated on several aerospace systems ranging from an unmanned aerial robot on the basis of an airship to the Propulsion and Reboost Subsystem of the Automated Transfer Vehicle (ATV), a supply spacecraft for the International Space Station.

  15. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

    PubMed

    Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

    2015-07-06

    Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

  16. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

    PubMed Central

    Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

    2015-01-01

    Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

  17. Computer-based diagnosis of illness in historical persons.

    PubMed

    Peters, T J

    2013-01-01

    Retrospective diagnosis of illness in historical figures is a popular but somewhat unreliable pastime due to the lack of detailed information and reliable reports about clinical features and disease progression. Modern computer-based diagnostic programmes have been used to supplement historical documents and accounts, offering new and more objective approaches to the retrospective investigations of the medical conditions of historical persons. In the case of King George III, modern technology has been used to strengthen the findings of previous reports rejecting the popular diagnosis of variegate porphyria in the King, his grandson Augustus d'Esté and his antecedent King James VI and I. Alternative diagnoses based on these programmes are indicated. The Operational Criteria in Studies of Psychotic Illness (OPCRIT) programme and the Young mania scale have been applied to the features described for George III and suggest a diagnosis of bipolar disorder. The neuro-diagnostic programme SimulConsult was applied to Augustus d'Esté and suggests a diagnosis of neuromyelitis optica rather than acute porphyria with secondarily multiple sclerosis, as proposed by others. James VI and I's complex medical history and the clinical features of his behavioural traits were also subjected to SimulConsult analysis; acute porphyria was rejected and the unexpected diagnosis of attenuated (mild) Lesch-Nyhan disease offered. A brief review of these approaches along with full reference listings to the methodology including validation are provided. Textual analysis of the written and verbal outputs of historical figures indicate possible future developments in the diagnosis of medical disorders in historical figures.

  18. Learning in the model space for cognitive fault diagnosis.

    PubMed

    Chen, Huanhuan; Tino, Peter; Rodan, Ali; Yao, Xin

    2014-01-01

    The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.

  19. Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models

    NASA Astrophysics Data System (ADS)

    Sharifi, Reza; Langari, Reza

    2017-02-01

    This paper presents a methodology for sensor fault diagnosis in nonlinear systems using a Mixture of Probabilistic Principal Component Analysis (MPPCA) models. This methodology separates the measurement space into several locally linear regions, each of which is associated with a Probabilistic PCA (PPCA) model. Using the transformation associated with each PPCA model, a parity relation scheme is used to construct a residual vector. Bayesian analysis of the residuals forms the basis for detection and isolation of sensor faults across the entire range of operation of the system. The resulting method is demonstrated in its application to sensor fault diagnosis of a fully instrumented HVAC system. The results show accurate detection of sensor faults under the assumption that a single sensor is faulty.

  20. Imaging-based diagnosis of acute renal allograft rejection

    PubMed Central

    Thölking, Gerold; Schuette-Nuetgen, Katharina; Kentrup, Dominik; Pawelski, Helga; Reuter, Stefan

    2016-01-01

    Kidney transplantation is the best available treatment for patients with end stage renal disease. Despite the introduction of effective immunosuppressant drugs, episodes of acute allograft rejection still endanger graft survival. Since efficient treatment of acute rejection is available, rapid diagnosis of this reversible graft injury is essential. For diagnosis of rejection, invasive core needle biopsy of the graft is the “gold-standard”. However, biopsy carries the risk of significant graft injury and is not immediately feasible in patients taking anticoagulants. Therefore, a non-invasive tool assessing the whole organ for specific and fast detection of acute allograft rejection is desirable. We herein review current imaging-based state of the art approaches for non-invasive diagnostics of acute renal transplant rejection. We especially focus on new positron emission tomography-based as well as targeted ultrasound-based methods. PMID:27011915

  1. An Intelligent Learning Diagnosis System for Web-Based Thematic Learning Platform

    ERIC Educational Resources Information Center

    Huang, Chenn-Jung; Liu, Ming-Chou; Chu, San-Shine; Cheng, Chih-Lun

    2007-01-01

    This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to…

  2. Reporting Test Outcomes Using Models for Cognitive Diagnosis. Research Report. ETS RR-06-28

    ERIC Educational Resources Information Center

    von Davier, Matthias; DiBello, Lou; Yamamoto, Kentaro

    2006-01-01

    Models for cognitive diagnosis have been developed as an attempt to provide more than a single test score from item response data. Most approaches are based on a hypothesis that relates items to underlying skills. This relation takes the form of a design matrix that specifies for each cognitive item which skills are required to solve the item and…

  3. Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

    ERIC Educational Resources Information Center

    Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

    2009-01-01

    This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

  4. Decision tree and PCA-based fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Sun, Weixiang; Chen, Jin; Li, Jiaqing

    2007-04-01

    After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

  5. A Cough-Based Algorithm for Automatic Diagnosis of Pertussis

    PubMed Central

    Pramono, Renard Xaviero Adhi; Imtiaz, Syed Anas; Rodriguez-Villegas, Esther

    2016-01-01

    Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. PMID:27583523

  6. Reflectance spectroscopy for diagnosis of epithelial precancer: model-based analysis of fiber-optic probe designs to resolve spectral information from epithelium and stroma

    PubMed Central

    Arifler, Dizem; Schwarz, Richard A.; Chang, Sung K.; Richards-Kortum, Rebecca

    2009-01-01

    Reflectance spectroscopy is a promising technology for detection of epithelial precancer. Fiber-optic probes that selectively collect scattered light from both the epithelium and the underlying stroma are likely to improve diagnostic performance of in vivo reflectance spectroscopy by revealing diagnostic features unique to each layer. We present Monte Carlo models with which to evaluate fiber-optic probe geometries with respect to sampling depth and depth resolution. We propose a probe design that utilizes half-ball lens coupled source and detector fibers to isolate epithelial scattering from stromal scattering and hence to resolve spectral information from the two layers. The probe is extremely compact and can provide easy access to different organ sites. PMID:16045217

  7. Diagnosis of Compartment Syndrome Based on Tissue Oxygenation

    DTIC Science & Technology

    2013-10-01

    measured with microprobes has been shown to be highly correlated with tissue oxygenation and the extent of ischemia reperfusion injury .3 Near-infrared...Tissue Oxygenation PRINCIPAL INVESTIGATOR: Hubert Kim, M.D., Ph.D. CONTRACTING ORGANIZATION: Northern California Institute for...SUBTITLE Diagnosis of Compartment Syndrome based on Tissue Oxygenation 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-10-1-1024 5c. PROGRAM ELEMENT

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

    NASA Astrophysics Data System (ADS)

    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.

  9. Condition Monitoring and Fault Diagnosis of Wet-Shift Clutch Transmission Based on Multi-technology

    NASA Astrophysics Data System (ADS)

    Chen, Man; Wang, Liyong; Ma, Biao

    Based on the construction feature and operating principle of the wet-shift clutch transmission, the condition monitoring and fault diagnosis for the transmission of the tracklayer with wet-shift clutch were implemented with using the oil analysis technology, function parameter test method and vibration analysis technology. The new fault diagnosis methods were proposed, which are to build the gray modeling with the oil analysis data, and to test the function parameter of the clutch press, the rotate speed of each gear, the oil press of the steer system and lubrication system and the hydraulic torque converter. It's validated that the representative function signals were chosen to execute the condition monitoring analysis, when the fault symptoms were found, and the oil analysis data were used to apply the gray modeling to forecast the fault occurs time can satisfy the demand of the condition monitoring and fault diagnosis for the transmission regular work.

  10. Self-calibrating models for dynamic monitoring and diagnosis

    NASA Technical Reports Server (NTRS)

    Kuipers, Benjamin

    1994-01-01

    The present goal in qualitative reasoning is to develop methods for automatically building qualitative and semiquantitative models of dynamic systems and to use them for monitoring and fault diagnosis. The qualitative approach to modeling provides a guarantee of coverage while our semiquantitative methods support convergence toward a numerical model as observations are accumulated. We have developed and applied methods for automatic creation of qualitative models, developed two methods for obtaining tractable results on problems that were previously intractable for qualitative simulation, and developed more powerful methods for learning semiquantitative models from observations and deriving semiquantitative predictions from them. With these advances, qualitative reasoning comes significantly closer to realizing its aims as a practical engineering method.

  11. Data mining and model simplicity: A case study in diagnosis

    SciTech Connect

    Provan, G.M.; Singh, M.

    1996-12-31

    We describe the results of performing data mining on a challenging medical diagnosis domain, acute abdominal pain. This domain is well known to be difficult, yielding little more than 60% predictive accuracy for most human and machine diagnosticians. Moreover, many researchers argue that one of the simplest approaches, the naive Bayesian classifier, is optimal. By comparing the performance of the naive Bayesian classifier to its more general cousin, the Bayesian network classifier, and to selective Bayesian classifiers with just 10% of the total attributes, we show that the simplest models perform at least as well as the more complex models. We argue that simple models like the selective naive Bayesian classifier will perform as well as more complicated models for similarly complex domains with relatively small data sets, thereby calling into question the extra expense necessary to induce more complex models.

  12. Vibration signal models for fault diagnosis of planetary gearboxes

    NASA Astrophysics Data System (ADS)

    Feng, Zhipeng; Zuo, Ming J.

    2012-10-01

    A thorough understanding of the spectral structure of planetary gear system vibration signals is helpful to fault diagnosis of planetary gearboxes. Considering both the amplitude modulation and the frequency modulation effects due to gear damage and periodically time variant working condition, as well as the effect of vibration transfer path, signal models of gear damage for fault diagnosis of planetary gearboxes are given and the spectral characteristics are summarized in closed form. Meanwhile, explicit equations for calculating the characteristic frequency of local and distributed gear fault are deduced. The theoretical derivations are validated using both experimental and industrial signals. According to the theoretical basis derived, manually created local gear damage of different levels and naturally developed gear damage in a planetary gearbox can be detected and located.

  13. BCC skin cancer diagnosis based on texture analysis techniques

    NASA Astrophysics Data System (ADS)

    Chuang, Shao-Hui; Sun, Xiaoyan; Chang, Wen-Yu; Chen, Gwo-Shing; Huang, Adam; Li, Jiang; McKenzie, Frederic D.

    2011-03-01

    In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% and 95%, respectively.

  14. PACS-Based Computer-Aided Detection and Diagnosis

    NASA Astrophysics Data System (ADS)

    Huang, H. K. (Bernie); Liu, Brent J.; Le, Anh HongTu; Documet, Jorge

    The ultimate goal of Picture Archiving and Communication System (PACS)-based Computer-Aided Detection and Diagnosis (CAD) is to integrate CAD results into daily clinical practice so that it becomes a second reader to aid the radiologist's diagnosis. Integration of CAD and Hospital Information System (HIS), Radiology Information System (RIS) or PACS requires certain basic ingredients from Health Level 7 (HL7) standard for textual data, Digital Imaging and Communications in Medicine (DICOM) standard for images, and Integrating the Healthcare Enterprise (IHE) workflow profiles in order to comply with the Health Insurance Portability and Accountability Act (HIPAA) requirements to be a healthcare information system. Among the DICOM standards and IHE workflow profiles, DICOM Structured Reporting (DICOM-SR); and IHE Key Image Note (KIN), Simple Image and Numeric Report (SINR) and Post-processing Work Flow (PWF) are utilized in CAD-HIS/RIS/PACS integration. These topics with examples are presented in this chapter.

  15. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

    PubMed

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

  16. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

    PubMed Central

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

  17. Autoregressive modelling for rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Al-Bugharbee, H.; Trendafilova, I.

    2015-07-01

    In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.

  18. Diagnosis and office-based treatment of urinary incontinence in adults. Part one: diagnosis and testing

    PubMed Central

    Heidelbaugh, Joel J.; Jimbo, Masahito

    2013-01-01

    Urinary incontinence is a common problem in both men and women. This review article addresses its prevalence, risk factors, cost, the various types of incontinence, as well as how to diagnose them. The US Preventive Services Task Force, the Cochrane Database of Systematic Reviews, and PubMed were reviewed for articles focusing on urinary incontinence. Incontinence is a common problem with a high societal cost. It is frequently underreported by patients so it is appropriate for primary-care providers to screen all women and older men during visits. A thorough history and physical examination combined with easy office-based tests can often yield a clear diagnosis and rule out other transient illnesses contributing to the incontinence. Specialist referral is occasionally needed in specific situations before embarking on a treatment plan. PMID:23904857

  19. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    PubMed

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing.

  20. Medical Diagnosis System of Breast Cancer Using FCM Based Parallel Neural Networks

    NASA Astrophysics Data System (ADS)

    Hwang, Sang-Hyun; Kim, Dongwon; Kang, Tae-Koo; Park, Gwi-Tae

    In this paper, a new methodology for medical diagnosis based on fuzzy clustering and parallel neural networks is proposed. Intelligent systems have various fields. Breast cancer is one of field to be targeted, which is the most common tumor-related disease among women. Diagnosis of breast cancer is not task for medical expert owing to many attributes of the disease. So we proposed a new method, FCM based parallel neural networks to handle difficult. FCM based parallel neural networks composed of two parts. One is classifying breast cancer data using Fuzzy c-means clustering method (FCM). The other is designing the multiple neural networks using classified data by FCM. The proposed methodology is experimented, evaluated, and compared the performance with other existed models. As a result we can show the effectiveness and precision of the proposed method are better than other previous models.

  1. A diagnosis system using object-oriented fault tree models

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; Patterson-Hine, F. A.

    1990-01-01

    Spaceborne computing systems must provide reliable, continuous operation for extended periods. Due to weight, power, and volume constraints, these systems must manage resources very effectively. A fault diagnosis algorithm is described which enables fast and flexible diagnoses in the dynamic distributed computing environments planned for future space missions. The algorithm uses a knowledge base that is easily changed and updated to reflect current system status. Augmented fault trees represented in an object-oriented form provide deep system knowledge that is easy to access and revise as a system changes. Given such a fault tree, a set of failure events that have occurred, and a set of failure events that have not occurred, this diagnosis system uses forward and backward chaining to propagate causal and temporal information about other failure events in the system being diagnosed. Once the system has established temporal and causal constraints, it reasons backward from heuristically selected failure events to find a set of basic failure events which are a likely cause of the occurrence of the top failure event in the fault tree. The diagnosis system has been implemented in common LISP using Flavors.

  2. Knowledge based acquisition of rules for medical diagnosis

    SciTech Connect

    Drastal, G.A.; Kulikowski, C.A.

    1982-01-01

    Medical consultation systems in the expert framework contain rules written under the guidance of expert physicians. The authors present a methodology and preliminary implementation of a system which learns compiled rule chains from positive case examples of a diagnostic class and negative examples of alternative diagnostic classes. Rule acquisition is guided by the constraints of physiological process models represented in the system. Evaluation of the system is proceeding in the area of glaucoma diagnosis, and an example of an experiment in this domain is included. 9 references.

  3. Sialendoscopy-based diagnosis and treatment of salivary ductal obstructions.

    PubMed

    Liao, Gui Qing; Su, Yu Xiong; Zheng, Guang Sen; Liang, Li Zhong

    2010-01-01

    Salivary gland ductal obstruction is traditionally treated by sialoadenectomy when conservative measures fail. During the last decade, sialendoscopy has become the preferred approach in the management of salivary ductal obstructions. Sialendoscopy can provide direct, accurate and reliable visualisation of the salivary duct lumen and ductal pathologies, and can eliminate pathologies with miniaturised instrumentation. Now, sialendoscopic surgery is a promising option for patients who can be offered a satisfactory clinical outcome while avoiding sialoadenectomy. The present article briefly outlines sialendoscopy-based diagnosis and treatment of salivary ductal obstructions.

  4. Tumors of the cranial base: Diagnosis and treatment

    SciTech Connect

    Sekhar, L.N.; Schramm, V.L.

    1987-01-01

    The first section of this book highlights the differences and similarities in the pathology and biology of the various types of neoplasms of the cranial base. The second section covers improvements in radiological diagnosis with the advent of computed tomography, magnetic resonance imaging and a better knowledge of radiological anatomy. It also examines the significance and proper evaluation of minor symptoms to enable earlier diagnosis, as well as the advances in interventional radiology that have produced the balloon occlusion text and tumor embolization. Section three is on advanced neuroanesthetic techniques and intraoperative neurophysiological monitoring. Section four describes specialized treatment modalities including microsurgical resection with the laser, radiation therapy and chemotherapy. Section five reviews the latest techniques for reconstruction of the cranial base following resection, as well as the preservation and reconstruction of cranial nerves and cerebral blood vessels exposed during the surgery. The final three sections examine the lesions and surgical techniques specific to the different anatomical regions, i.e, the anterior, middle and posterior cranial base.

  5. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    PubMed Central

    Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  6. Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

    PubMed

    Yin, Hong; Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

  7. Cancer Mortality in People Treated with Antidepressants before Cancer Diagnosis: A Population Based Cohort Study

    PubMed Central

    Sun, Yuelian; Vedsted, Peter; Fenger-Grøn, Morten; Wu, Chun Sen; Bech, Bodil Hammer; Olsen, Jørn; Benros, Michael Eriksen; Vestergaard, Mogens

    2015-01-01

    Background Depression is common after a cancer diagnosis and is associated with an increased mortality, but it is unclear whether depression occurring before the cancer diagnosis affects cancer mortality. We aimed to study cancer mortality of people treated with antidepressants before cancer diagnosis. Methods and Findings We conducted a population based cohort study of all adults diagnosed with cancer between January 2003 and December 2010 in Denmark (N = 201,662). We obtained information on cancer from the Danish Cancer Registry, on the day of death from the Danish Civil Registry, and on redeemed antidepressants from the Danish National Prescription Registry. Current users of antidepressants were defined as those who redeemed the latest prescription of antidepressant 0–4 months before cancer diagnosis (irrespective of earlier prescriptions), and former users as those who redeemed the latest prescription five or more months before cancer diagnosis. We estimated an all-cause one-year mortality rate ratio (MRR) and a conditional five-year MRR for patients who survived the first year after cancer diagnosis and confidence interval (CI) using a Cox proportional hazards regression model. Overall, 33,111 (16.4%) patients redeemed at least one antidepressant prescription in the three years before cancer diagnosis of whom 21,851 (10.8%) were current users at the time of cancer diagnosis. Current antidepressant users had a 32% higher one-year mortality (MRR = 1.32, 95% CI: 1.29–1.35) and a 22% higher conditional five-year mortality (MRR = 1.22, 95% CI: 1.17–1.26) if patients survived the first year after the cancer diagnosis than patients not redeeming antidepressants. The one-year mortality was particularly high for patients who initiated antidepressant treatment within four months before cancer diagnosis (MRR = 1.54, 95% CI: 1.47–1.61). Former users had no increased cancer mortality. Conclusions Initiation of antidepressive treatment prior to cancer diagnosis is

  8. Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model.

    PubMed

    Khodayari-Rostamabad, Ahmad; Reilly, James P; Hasey, Gary; Debruin, Hubert; Maccrimmon, Duncan

    2010-01-01

    An automated diagnosis procedure based on a statistical machine learning methodology using electroencephalograph (EEG) data is proposed for diagnosis of psychiatric illness. First, a large collection of candidate features, mostly consisting of various statistical quantities, are calculated from the subject's EEG. This large set of candidate features is then reduced into a much smaller set of most relevant features using a feature selection procedure. The selected features are then used to evaluate the class likelihoods, through the use of a mixture of factor analysis (MFA) statistical model [7]. In a training set of 207 subjects, including 64 subjects with major depressive disorder (MDD), 40 subjects with chronic schizophrenia, 12 subjects with bipolar depression and 91 normal or healthy subjects, the average correct diagnosis rate attained using the proposed method is over 85%, as determined by various cross-validation experiments. The promise is that, with further development, the proposed methodology could serve as a valuable adjunctive tool for the medical practitioner.

  9. Adaptive PCA based fault diagnosis scheme in imperial smelting process.

    PubMed

    Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

    2014-09-01

    In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently.

  10. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    DOE PAGES

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

  11. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    SciTech Connect

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; Sheng, Shuangwen

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issue is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.

  12. Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Mengshoel, Ole

    2008-01-01

    Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

  13. A failure diagnosis system based on a neural network classifier for the space shuttle main engine

    NASA Technical Reports Server (NTRS)

    Duyar, Ahmet; Merrill, Walter

    1990-01-01

    A conceptual design of a model based failure detection and diagnosis system is developed for the space shuttle main engine. This design relies on the accurate and reliable identification of the parameters of the highly nonlinear and very complex engine. The design approach is presented in some detail and results for a failed valve are presented. These preliminary results verify that the developed parameter identification technique together with a neural network classifier can be used for this purpose.

  14. Knowledge-based and integrated monitoring and diagnosis in autonomous power systems

    NASA Technical Reports Server (NTRS)

    Momoh, J. A.; Zhang, Z. Z.

    1990-01-01

    A new technique of knowledge-based and integrated monitoring and diagnosis (KBIMD) to deal with abnormalities and incipient or potential failures in autonomous power systems is presented. The KBIMD conception is discussed as a new function of autonomous power system automation. Available diagnostic modelling, system structure, principles and strategies are suggested. In order to verify the feasibility of the KBIMD, a preliminary prototype expert system is designed to simulate the KBIMD function in a main electric network of the autonomous power system.

  15. Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

    NASA Astrophysics Data System (ADS)

    Zhang, Han

    Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

  16. NGS-based Molecular diagnosis of 105 eyeGENE(®) probands with Retinitis Pigmentosa.

    PubMed

    Ge, Zhongqi; Bowles, Kristen; Goetz, Kerry; Scholl, Hendrik P N; Wang, Feng; Wang, Xinjing; Xu, Shan; Wang, Keqing; Wang, Hui; Chen, Rui

    2015-12-15

    The National Ophthalmic Disease Genotyping and Phenotyping Network (eyeGENE(®)) was established in an effort to facilitate basic and clinical research of human inherited eye disease. In order to provide high quality genetic testing to eyeGENE(®)'s enrolled patients which potentially aids clinical diagnosis and disease treatment, we carried out a pilot study and performed Next-generation sequencing (NGS) based molecular diagnosis for 105 Retinitis Pigmentosa (RP) patients randomly selected from the network. A custom capture panel was designed, which incorporated 195 known retinal disease genes, including 61 known RP genes. As a result, disease-causing mutations were identified in 52 out of 105 probands (solving rate of 49.5%). A total of 82 mutations were identified, and 48 of them were novel. Interestingly, for three probands the molecular diagnosis was inconsistent with the initial clinical diagnosis, while for five probands the molecular information suggested a different inheritance model other than that assigned by the physician. In conclusion, our study demonstrated that NGS target sequencing is efficient and sufficiently precise for molecular diagnosis of a highly heterogeneous patient cohort from eyeGENE(®).

  17. Near Real-Time Probabilistic Damage Diagnosis Using Surrogate Modeling and High Performance Computing

    NASA Technical Reports Server (NTRS)

    Warner, James E.; Zubair, Mohammad; Ranjan, Desh

    2017-01-01

    This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation.

  18. Microfluidic chip-based technologies: emerging platforms for cancer diagnosis

    PubMed Central

    2013-01-01

    The development of early and personalized diagnostic protocols is considered the most promising avenue to decrease mortality from cancer and improve outcome. The emerging microfluidic-based analyzing platforms hold high promises to fulfill high-throughput and high-precision screening with reduced equipment cost and low analysis time, as compared to traditional bulky counterparts in bench-top laboratories. This article overviewed the potential applications of microfluidic technologies for detection and monitoring of cancer through nucleic acid and protein biomarker analysis. The implications of the technologies in cancer cytology that can provide functional personalized diagnosis were highlighted. Finally, the future niches for using microfluidic-based systems in tumor screening were briefly discussed. PMID:24070124

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

    PubMed Central

    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. PMID:25938760

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

    PubMed

    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.

  1. A Clinical Model for the Diagnosis and Management of Patients with Cervical Spine Syndromes

    PubMed Central

    Murphy, Donald R.

    2004-01-01

    Background: Disorders of the cervical spine are common and often disabling. The etiology of these disorders is often multifactorial and a comprehensive approach to both diagnosis and management is essential to successful resolution. Objective: This article provides an overview of a clinical model of the diagnosis and management of patients with disorders related to the cervical spine. This model is based in part on the scientific literature, clinical experience, and communication with other practitioners over the course of the past 20 years. Discussion: The clinical model presented here involves taking a systematic approach to diagnosis, and management. The diagnostic process is one that asks three essential questions. The answers to these questions then guides the management process, allowing the physician to apply specific methods that address the many factors that can be involved in each individual patient. This clinical model allows the physician to individualize the management strategy while utilizing principles that can be applied to all patients. At times, the management strategy must be multidisciplinary, and cooperation with other physicians and therapists is often necessary for effective patient care. This model is currently being used by the author in practice, as well as forming the basis upon which further research can be conducted to refine or, if necessary, abandon any of its aspects, as the evidence dictates. It is the purpose of this paper to present this clinical model and the clinical and scientific evidence, or lack thereof, of its components. PMID:17987214

  2. Compound fault diagnosis of gearboxes based on GFT component extraction

    NASA Astrophysics Data System (ADS)

    Ou, Lu; Yu, Dejie

    2016-11-01

    Compound fault diagnosis of gearboxes is of great importance to the long-term safe operation of rotating machines, and the key is to separate different fault components. In this paper, the path graph is introduced into the vibration signal analysis and the graph Fourier transform (GFT) of vibration signals are investigated from the graph spectrum domain. To better extract the fault components in gearboxes, a new adjacency weight matrix is defined and then the GFT of simulation signals of the gear and the bearing with localized faults are analyzed. Further, since the GFT graph spectrum of the gear fault component and the bearing fault component are mainly distributed in the low-order region and the high-order region, respectively, a novel method for the compound fault diagnosis of gearboxes based on GFT component extraction is proposed. In this method, the nonzero ratios, which are introduced to analyze the eigenvectors auxiliary, and the GFT of a gearbox vibration signal, are firstly calculated. Then, the order thresholds for reconstructed fault components are determined and the fault components are extracted. Finally, the Hilbert demodulation analyses are conducted. According to the envelope spectra of the fault components, the faults of the gear and the bearing can be diagnosed respectively. The performance of the proposed method is validated by the simulation data and the experiment signals from a gearbox with compound faults.

  3. Robustness of Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Templin, Jonathan L.; Henson, Robert A.; Templin, Sara E.; Roussos, Louis

    2008-01-01

    Several types of parameterizations of attribute correlations in cognitive diagnosis models use the reduced reparameterized unified model. The general approach presumes an unconstrained correlation matrix with K(K - 1)/2 parameters, whereas the higher order approach postulates K parameters, imposing a unidimensional structure on the correlation…

  4. Computational complexity issues in operative diagnosis of graph-based systems

    SciTech Connect

    Rao, N.S.V. )

    1993-04-01

    Systems that can be modeled as graphs, such that nodes represent the components and the edges represent the fault propagation between the components, are considered. Some components are equipped with alarms that ring in response to faulty conditions. In these systems, two types of problems are studied: (a) fault diagnosis, and (b) alarm placement. The fault diagnosis problems deal with computing the set of all potential failure sources that correspond to a set of ringing alarms A[sub R]. First, the single faults, where exactly one component can become faulty at any time, are considered. Systems are classified into zero-time and nonzero-time systems based on fault propagation times, and the latter is further classified based on the knowledge of propagation times. For each of these classes algorithms are presented for single fault diagnosis. The problem of detecting multiple faults is shown to be NP-complete. An alarm placement problem, that requires a single fault to be uniquely diagnosed, is examined; various versions of this problem are shown to be NP-complete. The single fault diagnosis algorithms have been implemented and tested.

  5. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    NASA Technical Reports Server (NTRS)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1996-01-01

    Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

  6. Diagnosis of Dengue Infection Using Conventional and Biosensor Based Techniques

    PubMed Central

    Parkash, Om; Hanim Shueb, Rafidah

    2015-01-01

    Dengue is an arthropod-borne viral disease caused by four antigenically different serotypes of dengue virus. This disease is considered as a major public health concern around the world. Currently, there is no licensed vaccine or antiviral drug available for the prevention and treatment of dengue disease. Moreover, clinical features of dengue are indistinguishable from other infectious diseases such as malaria, chikungunya, rickettsia and leptospira. Therefore, prompt and accurate laboratory diagnostic test is urgently required for disease confirmation and patient triage. The traditional diagnostic techniques for the dengue virus are viral detection in cell culture, serological testing, and RNA amplification using reverse transcriptase PCR. This paper discusses the conventional laboratory methods used for the diagnosis of dengue during the acute and convalescent phase and highlights the advantages and limitations of these routine laboratory tests. Subsequently, the biosensor based assays developed using various transducers for the detection of dengue are also reviewed. PMID:26492265

  7. Plantar fasciitis: evidence-based review of diagnosis and therapy.

    PubMed

    Cole, Charles; Seto, Craig; Gazewood, John

    2005-12-01

    Plantar fasciitis causes heel pain in active as well as sedentary adults of all ages. The condition is more likely to occur in persons who are obese or in those who are on their feet most of the day. A diagnosis of plantar fasciitis is based on the patient's history and physical findings. The accuracy of radiologic studies in diagnosing plantar heel pain is unknown. Most interventions used to manage plantar fasciitis have not been studied adequately; however, shoe inserts, stretching exercises, steroid injection, and custom-made night splints may be beneficial. Extracorporeal shock wave therapy may effectively treat runners with chronic heel pain but is ineffective in other patients. Limited evidence suggests that casting or surgery may be beneficial when conservative measures fail.

  8. Diagnosis of Dengue Infection Using Conventional and Biosensor Based Techniques.

    PubMed

    Parkash, Om; Shueb, Rafidah Hanim

    2015-10-19

    Dengue is an arthropod-borne viral disease caused by four antigenically different serotypes of dengue virus. This disease is considered as a major public health concern around the world. Currently, there is no licensed vaccine or antiviral drug available for the prevention and treatment of dengue disease. Moreover, clinical features of dengue are indistinguishable from other infectious diseases such as malaria, chikungunya, rickettsia and leptospira. Therefore, prompt and accurate laboratory diagnostic test is urgently required for disease confirmation and patient triage. The traditional diagnostic techniques for the dengue virus are viral detection in cell culture, serological testing, and RNA amplification using reverse transcriptase PCR. This paper discusses the conventional laboratory methods used for the diagnosis of dengue during the acute and convalescent phase and highlights the advantages and limitations of these routine laboratory tests. Subsequently, the biosensor based assays developed using various transducers for the detection of dengue are also reviewed.

  9. Design and implementation of virtual fault diagnosis system for photoelectric tracking devices based on OpenGL

    NASA Astrophysics Data System (ADS)

    Hou, MingLiang; Li, Cunhua; Zhang, Yong; Su, Liyun

    2009-10-01

    In view of the crucial deficiency of the traditional diagnosis approaches for photoelectric tracking devices and the output of more sufficient diagnosis information, in this paper, an virtual fault diagnosis system based on open graphic library(OpenGL) is proposed. Firstly, some interrelated key principles and technology of virtual reality, visualization and intelligent fault diagnosis technology are put forward. Then, the demand analysis and architecture of the system are elaborated. Next, details of interrelated essential implementation issues are also discussed, including the the 3D modeling of the related diagnosis equipments, key development process and design via OpenGL. Practical applications and experiments illuminate that the proposed approach is feasible and effective.

  10. Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

    NASA Astrophysics Data System (ADS)

    Ben Rabah, N.; Saddem, R.; Ben Hmida, F.; Carre-Menetrier, V.; Tagina, M.

    2017-01-01

    Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach.

  11. Deterministic versus evidence-based attitude towards clinical diagnosis.

    PubMed

    Soltani, Akbar; Moayyeri, Alireza

    2007-08-01

    Generally, two basic classes have been proposed for scientific explanation of events. Deductive reasoning emphasizes on reaching conclusions about a hypothesis based on verification of universal laws pertinent to that hypothesis, while inductive or probabilistic reasoning explains an event by calculation of some probabilities for that event to be related to a given hypothesis. Although both types of reasoning are used in clinical practice, evidence-based medicine stresses on the advantages of the second approach for most instances in medical decision making. While 'probabilistic or evidence-based' reasoning seems to involve more mathematical formulas at the first look, this attitude is more dynamic and less imprisoned by the rigidity of mathematics comparing with 'deterministic or mathematical attitude'. In the field of medical diagnosis, appreciation of uncertainty in clinical encounters and utilization of likelihood ratio as measure of accuracy seem to be the most important characteristics of evidence-based doctors. Other characteristics include use of series of tests for refining probability, changing diagnostic thresholds considering external evidences and nature of the disease, and attention to confidence intervals to estimate uncertainty of research-derived parameters.

  12. Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters

    NASA Astrophysics Data System (ADS)

    Lu, Yunsong; Wang, Fuli; Jia, Mingxing; Qi, Yuanchen

    2016-12-01

    This paper concerns fault diagnosis of centrifugal compressor based on thermal parameters. An improved qualitative simulation (QSIM) based fault diagnosis method is proposed to diagnose the faults of centrifugal compressor in a gas-steam combined-cycle power plant (CCPP). The qualitative models under normal and two faulty conditions have been built through the analysis of the principle of centrifugal compressor. To solve the problem of qualitative description of the observations of system variables, a qualitative trend extraction algorithm is applied to extract the trends of the observations. For qualitative states matching, a sliding window based matching strategy which consists of variables operating ranges constraints and qualitative constraints is proposed. The matching results are used to determine which QSIM model is more consistent with the running state of system. The correct diagnosis of two typical faults: seal leakage and valve stuck in the centrifugal compressor has validated the targeted performance of the proposed method, showing the advantages of fault roots containing in thermal parameters.

  13. Applied Neuroimaging to the Diagnosis of Alzheimer's Disease: A Multicriteria Model

    NASA Astrophysics Data System (ADS)

    Tamanini, Isabelle; de Castro, Ana Karoline; Pinheiro, Plácido Rogério; Pinheiro, Mirian Calíope Dantas

    In the last few years, Alzheimer's disease has been the most frequent cause of dementia and it is responsible, alone or in association with other diseases, for 50% of the cases in western countries. Dementias are syndromes characterized by a decline in memory and other neuropsychological changes, especially occurring in elderly people and increasing exponentially along the aging process. The main focus of this work is to develop a multicriteria model for aiding in decision making on the diagnosis of Alzheimer's disease by using the Aranau Tool, structured on the Verbal Decision Analysis. In this work, the modeling and evaluation processes were conducted with the aid of a medical expert, bibliographic sources and questionnaires. The questionnaires taken into account were based mainly on patients' neuroimaging tests, and we analyzed wheter or not there were problems in the patients' brain that could be relevant to the diagnosis of Alzheimer's disease.

  14. The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator.

    PubMed

    Roh, S D; Kim, S W; Cho, W S

    2001-10-01

    The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control.

  15. Specific contrast ultrasound using sterically stabilized microbubbles for early diagnosis of thromboembolic disease in a rabbit model.

    PubMed

    Vlašín, Michal; Lukáč, Robert; Kauerová, Zuzana; Kohout, Pavel; Mašek, Josef; Bartheldyová, Eliška; Koudelka, Štěpán; Korvasová, Zina; Plocková, Jana; Hronová, Nikola; Turánek, Jaroslav

    2014-04-01

    Specific contrast ultrasound is widely applied in diagnostic procedures on humans but remains underused in veterinary medicine. The objective of this study was to evaluate the use of microbubble-based contrast for rapid ultrasonographic diagnosis of thrombosis in small animals, using male New Zealand white rabbits (average weight about 3.5 kg) as a model. It was hypothesized that the use of microbubble-based contrast agents will result in a faster and more precise diagnosis in our model of thrombosis. A pro-coagulant environment had been previously established by combining endothelial denudation and external vessel wall damage. Visualization of thrombi was achieved by application of contrast microbubbles [sterically stabilized, phospholipid-based microbubbles filled with sulfur hexafluoride (SF₆) gas] and ultrasonography. As a result, rapid and clear diagnosis of thrombi in aorta abdominalis was achieved within 10 to 30 s (mean: 17.3 s) by applying microbubbles as an ultrasound contrast medium. In the control group, diagnosis was not possible or took 90 to 180 s. Therefore, sterically stabilized microbubbles were found to be a suitable contrast agent for the rapid diagnosis of thrombi in an experimental model in rabbits. This contrast agent could be of practical importance in small animal practice for rapid diagnosis of thrombosis.

  16. Physically based diagnosis and prognosis of cracked rotor shafts

    NASA Astrophysics Data System (ADS)

    Oppenheimer, Charles H.; Loparo, Kenneth A.

    2002-07-01

    A physics-based approach for diagnostics and prognostics using integrated observers and life models is presented. Observers are filters based on physical models of machine- fault combinations and use measured machine signatures to identify and characterize the state of a machine. Observers are adaptively deployed as a machine wears and can be coupled with one another to handle interacting conditions and faults. The scheme is detailed using the fault of a cracked rotor shaft that interacts with gravity and imbalance. Observers for shaft cracking and imbalance are presented. The observers provide machine condition and fault strengths to life models used to determine remaining machine life. A life model based on the Forman crack growth law of linear elastic fracture mechanics is developed to determine the number of machine cycles remaining until catastrophic failure.

  17. Modeling power systems for diagnosis - How good is good enough?

    NASA Astrophysics Data System (ADS)

    Fesq, Lorraine M.; Stephan, Amy; McNamee, Lawrence

    Space power system automation requires comprehensive fault detection, isolation and recovery. Extending current approaches to on-board power management to isolate and resolve (as well as detect) failures would greatly increase the size and complexity of the flight software. Instead, we identified a technique called model-based constraint suspension which can isolate a large percentage of failures, including unforeseen failures, with minimal increases in code size. Adapting this digital technique to an electrical power system required us to address several key issues including model construction and fidelity. This paper contrasts several approaches to model-based diagnostics to our diagnostic system, Marple, which has been demonstrated on a power testbed.

  18. Model-Based Sensor Selection for Helicopter Gearbox Monitoring

    DTIC Science & Technology

    1996-04-01

    fault diagnosis of helicopter gearboxes is therefore necessary to prevent major breakdowns due to progression of undetected...in the gearbox . Once the presence of a fault is prompted by the fault detection network, fault diagnosis is performed by the Structure-Based...Components Figure 3: Overview of fault detection and diagnosis in the proposed model-based di- agnostic system for helicopter gearboxes . the OH-58A gearbox

  19. Are diagnosis specific outcome indicators based on administrative data useful in assessing quality of hospital care?

    PubMed Central

    Scott, I; Youlden, D; Coory, M

    2004-01-01

    Background: Hospital performance reports based on administrative data should distinguish differences in quality of care between hospitals from case mix related variation and random error effects. A study was undertaken to determine which of 12 diagnosis-outcome indicators measured across all hospitals in one state had significant risk adjusted systematic (or special cause) variation (SV) suggesting differences in quality of care. For those that did, we determined whether SV persists within hospital peer groups, whether indicator results correlate at the individual hospital level, and how many adverse outcomes would be avoided if all hospitals achieved indicator values equal to the best performing 20% of hospitals. Methods: All patients admitted during a 12 month period to 180 acute care hospitals in Queensland, Australia with heart failure (n = 5745), acute myocardial infarction (AMI) (n = 3427), or stroke (n = 2955) were entered into the study. Outcomes comprised in-hospital deaths, long hospital stays, and 30 day readmissions. Regression models produced standardised, risk adjusted diagnosis specific outcome event ratios for each hospital. Systematic and random variation in ratio distributions for each indicator were then apportioned using hierarchical statistical models. Results: Only five of 12 (42%) diagnosis-outcome indicators showed significant SV across all hospitals (long stays and same diagnosis readmissions for heart failure; in-hospital deaths and same diagnosis readmissions for AMI; and in-hospital deaths for stroke). Significant SV was only seen for two indicators within hospital peer groups (same diagnosis readmissions for heart failure in tertiary hospitals and inhospital mortality for AMI in community hospitals). Only two pairs of indicators showed significant correlation. If all hospitals emulated the best performers, at least 20% of AMI and stroke deaths, heart failure long stays, and heart failure and AMI readmissions could be avoided

  20. Biophotonics in diagnosis and modeling of tissue pathologies

    NASA Astrophysics Data System (ADS)

    Serafetinides, A. A.; Makropoulou, M.; Drakaki, E.

    2008-12-01

    Biophotonics techniques are applied to several fields in medicine and biology. The laser based techniques, such as the laser induced fluorescence (LIF) spectroscopy and the optical coherence tomography (OCT), are of particular importance in dermatology, where the laser radiation could be directly applied to the tissue target (e.g. skin). In addition, OCT resolves architectural tissue properties that might be useful as tumour discrimination parameters for skin as well as for ocular non-invasive visualization. Skin and ocular tissues are complex multilayered and inhomogeneous organs with spatially varying optical properties. This fact complicates the quantitative analysis of the fluorescence and/or light scattering spectra, even from the same tissue sample. To overcome this problem, mathematical simulation is applied for the investigation of the human tissue optical properties, in the visible/infrared range of the spectrum, resulting in a better discrimination of several tissue pathologies. In this work, we present i) a general view on biophotonics applications in diagnosis of human diseases, ii) some specific results on laser spectroscopy techniques, as LIF measurements, applied in arterial and skin pathologies and iii) some experimental and theoretical results on ocular OCT measurements. Regarding the LIF spectroscopy, we examined the autofluorescence properties of several human skin samples, excised from humans undergoing biopsy examination. A nitrogen laser was used as an excitation source, emitting at 337 nm (ultraviolet excitation). Histopathology examination of the samples was also performed, after the laser spectroscopy measurements and the results from the spectroscopic and medical analysis were compared, to differentiate malignancies, e.g. basal cell carcinoma tissue (BCC), from normal skin tissue. Regarding the OCT technique, we correlated human data, obtained from patients undergoing OCT examination, with Monte Carlo simulated cornea and retina tissues

  1. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    PubMed

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

  2. Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis.

    PubMed

    Lu, Chuan; Devos, Andy; Suykens, Johan A K; Arús, Carles; Van Huffel, Sabine

    2007-05-01

    This paper investigates variable selection (VS) and classification for biomedical datasets with a small sample size and a very high input dimension. The sequential sparse Bayesian learning methods with linear bases are used as the basic VS algorithm. Selected variables are fed to the kernel-based probabilistic classifiers: Bayesian least squares support vector machines (BayLS-SVMs) and relevance vector machines (RVMs). We employ the bagging techniques for both VS and model building in order to improve the reliability of the selected variables and the predictive performance. This modeling strategy is applied to real-life medical classification problems, including two binary cancer diagnosis problems based on microarray data and a brain tumor multiclass classification problem using spectra acquired via magnetic resonance spectroscopy. The work is experimentally compared to other VS methods. It is shown that the use of bagging can improve the reliability and stability of both VS and model prediction.

  3. Photoacoustic-based nanomedicine for cancer diagnosis and therapy.

    PubMed

    Sim, Changbeom; Kim, Haemin; Moon, Hyungwon; Lee, Hohyeon; Chang, Jin Ho; Kim, Hyuncheol

    2015-04-10

    Photoacoustic imaging is the latest promising diagnostic modality that has various advantages such as high spatial resolution, deep penetration depth, and use of non-ionizing radiation. It also employs a non-invasive imaging technique and optically functionalized imaging. The goal of this study was to develop a nanomedicine for simultaneous cancer therapy and diagnosis based on photoacoustic imaging. Human serum albumin nanoparticles loaded with melanin and paclitaxel (HMP-NPs) were developed using the desolvation technique. The photoacoustic-based diagnostic and chemotherapeutic properties of HMP-NPs were evaluated through in vitro and in vivo experiments. The size and zeta potential of the HMP-NPs were found to be 192.8±21.11nm and -22.2±4.39mV, respectively. In in vitro experiments, HMP-NPs produced increased photoacoustic signal intensity because of the loaded melanin and decreased cellular viability because of the encapsulated paclitaxel, compared to the free human serum albumin nanoparticles (the control). In vivo experiments showed that the HMP-NPs efficiently accumulated inside the tumor, resulting in the enhanced photoacoustic signal intensity in the tumor site, compared to the normal tissues. The in vivo chemotherapy study demonstrated that HMP-NPs had the capability to treat cancer for an extended period. In conclusion, HMP-NPs were simultaneously capable of photoacoustic diagnostic and chemotherapy against cancer.

  4. Computer-aided diagnosis workstation and database system for chest diagnosis based on multihelical CT images

    NASA Astrophysics Data System (ADS)

    Sato, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Moriyama, Noriyuki; Ohmatsu, Hironobu; Kakinuma, Ryutaro; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

    2004-04-01

    Lung cancer is the most common cause, accounting for about 20% of all cancer deaths for males in Japan. Myocardial infarction is also known as a most fearful adult disease. Recently, multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for screening examination. This screening examination requires a considerable number of images to be read. It is this time-consuming step that makes the use of multi-helical CT for mass screening. To overcome this problem, our group has developed a computer-aided diagnosis algorithm to automatically detect suspicious regions of lung cancer and coronary calcifications in chest CT images, so far. And in this time, our group has developed a newly computer-aided diagnosis workstation and database. These consist in three. First, it is an image processing system to automatically detect suspicious bronchial regions, pulmonary artery regions, plumonary vein regions and myocardial infarction regions at high speed. Second, they are two 1600 x 1200 matrix black and white liquid crystal monitor. Third, it is a terminal of image storage. These are connected mutually on the network. This makes it much easier to read images, since the 3D image of suspicious regions and shadow of suspicious regions can be displayed simultaneously on two 1600 x 1200 matrix liquid crystal monitor. The experimental results indicate that a newly computer-aided diagnosis workstation and database system can be effectively used in clinical practice to increase the speed and accuracy of routine diagnosis.

  5. Assessment of Differential Item Functioning under Cognitive Diagnosis Models: The DINA Model Example

    ERIC Educational Resources Information Center

    Li, Xiaomin; Wang, Wen-Chung

    2015-01-01

    The assessment of differential item functioning (DIF) is routinely conducted to ensure test fairness and validity. Although many DIF assessment methods have been developed in the context of classical test theory and item response theory, they are not applicable for cognitive diagnosis models (CDMs), as the underlying latent attributes of CDMs are…

  6. Fault diagnosis in spur gears based on genetic algorithm and random forest

    NASA Astrophysics Data System (ADS)

    Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  7. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants.

    PubMed

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-08-22

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.

  8. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

    PubMed Central

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-01-01

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. PMID:27556472

  9. Empirical Mode Decomposition Based Features for Diagnosis and Prognostics of Systems

    DTIC Science & Technology

    2008-04-01

    bearing fault diagnosis – their effectiveness and flexibilities. Journal of Vibration and Acoustics July 2001, ASME. 3. Staszewski, W. J. Structural...Empirical Mode Decomposition Based Features for Diagnosis and Prognostics of Systems by Hiralal Khatri, Kenneth Ranney, Kwok Tom, and Romeo...Laboratory Adelphi, MD 20783-1197 ARL-TR-4301 April 2008 Empirical Mode Decomposition Based Features for Diagnosis and Prognostics of Systems

  10. Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records

    PubMed Central

    Emir, Birol; Masters, Elizabeth T; Mardekian, Jack; Clair, Andrew; Kuhn, Max; Silverman, Stuart L

    2015-01-01

    Background Diagnosis of fibromyalgia (FM), a chronic musculoskeletal condition characterized by widespread pain and a constellation of symptoms, remains challenging and is often delayed. Methods Random forest modeling of electronic medical records was used to identify variables that may facilitate earlier FM identification and diagnosis. Subjects aged ≥18 years with two or more listings of the International Classification of Diseases, Ninth Revision, (ICD-9) code for FM (ICD-9 729.1) ≥30 days apart during the 2012 calendar year were defined as cases among subjects associated with an integrated delivery network and who had one or more health care provider encounter in the Humedica database in calendar years 2011 and 2012. Controls were without the FM ICD-9 codes. Seventy-two demographic, clinical, and health care resource utilization variables were entered into a random forest model with downsampling to account for cohort imbalances (<1% subjects had FM). Importance of the top ten variables was ranked based on normalization to 100% for the variable with the largest loss in predicting performance by its omission from the model. Since random forest is a complex prediction method, a set of simple rules was derived to help understand what factors drive individual predictions. Results The ten variables identified by the model were: number of visits where laboratory/non-imaging diagnostic tests were ordered; number of outpatient visits excluding office visits; age; number of office visits; number of opioid prescriptions; number of medications prescribed; number of pain medications excluding opioids; number of medications administered/ordered; number of emergency room visits; and number of musculoskeletal conditions. A receiver operating characteristic curve confirmed the model’s predictive accuracy using an independent test set (area under the curve, 0.810). To enhance interpretability, nine rules were developed that could be used with good predictive probability of

  11. Evidence-based diagnosis and management of acute bronchitis.

    PubMed

    Hart, Ann Marie

    2014-09-18

    Acute bronchitis is a common respiratory infection seen in primary care settings. This article examines the current evidence for diagnosis and management of acute bronchitis in adults and provides recommendations for primary care clinical practice.

  12. A Note on Comparing Examinee Classification Methods for Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Huebner, Alan; Wang, Chun

    2011-01-01

    Cognitive diagnosis models have received much attention in the recent psychometric literature because of their potential to provide examinees with information regarding multiple fine-grained discretely defined skills, or attributes. This article discusses the issue of methods of examinee classification for cognitive diagnosis models, which are…

  13. A general proof of consistency of heuristic classification for cognitive diagnosis models.

    PubMed

    Chiu, Chia-Yi; Köhn, Hans-Friedrich

    2015-11-01

    The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633-665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non-parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non-parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.

  14. Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; George, Laurence L.; Patterson-Hine, F. A.; Lum, Henry, Jr. (Technical Monitor)

    1994-01-01

    When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be

  15. Continuous wavelet transform-based feature selection applied to near-infrared spectral diagnosis of cancer.

    PubMed

    Chen, Hui; Lin, Zan; Mo, Lin; Wu, Hegang; Wu, Tong; Tan, Chao

    2015-01-01

    Spectrum is inherently local in nature since it can be thought of as a signal being composed of various frequency components. Wavelet transform (WT) is a powerful tool that partitions a signal into components with different frequency. The property of multi-resolution enables WT a very effective and natural tool for analyzing spectrum-like signal. In this study, a continuous wavelet transform (CWT)-based variable selection procedure was proposed to search for a set of informative wavelet coefficients for constructing a near-infrared (NIR) spectral diagnosis model of cancer. The CWT provided a fine multi-resolution feature space for selecting best predictors. A measure of discriminating power (DP) was defined to evaluate the coefficients. Partial least squares-discriminant analysis (PLS-DA) was used as the classification algorithm. A NIR spectral dataset associated to cancer diagnosis was used for experiment. The optimal results obtained correspond to the wavelet of db2. It revealed that on condition of having better performance on the training set, the optimal PLS-DA model using only 40 wavelet coefficients in 10 scales achieved the same performance as the one using all the variables in the original space on the test set: an overall accuracy of 93.8%, sensitivity of 92.5% and specificity of 96.3%. It confirms that the CWT-based feature selection coupled with PLS-DA is feasible and effective for constructing models of diagnostic cancer by NIR spectroscopy.

  16. Base Flow Model Validation

    NASA Technical Reports Server (NTRS)

    Sinha, Neeraj; Brinckman, Kevin; Jansen, Bernard; Seiner, John

    2011-01-01

    A method was developed of obtaining propulsive base flow data in both hot and cold jet environments, at Mach numbers and altitude of relevance to NASA launcher designs. The base flow data was used to perform computational fluid dynamics (CFD) turbulence model assessments of base flow predictive capabilities in order to provide increased confidence in base thermal and pressure load predictions obtained from computational modeling efforts. Predictive CFD analyses were used in the design of the experiments, available propulsive models were used to reduce program costs and increase success, and a wind tunnel facility was used. The data obtained allowed assessment of CFD/turbulence models in a complex flow environment, working within a building-block procedure to validation, where cold, non-reacting test data was first used for validation, followed by more complex reacting base flow validation.

  17. DERMA: A Melanoma Diagnosis Platform Based on Collaborative Multilabel Analog Reasoning

    PubMed Central

    Golobardes, Elisabet; Corral, Guiomar; Puig, Susana; Malvehy, Josep

    2014-01-01

    The number of melanoma cancer-related death has increased over the last few years due to the new solar habits. Early diagnosis has become the best prevention method. This work presents a melanoma diagnosis architecture based on the collaboration of several multilabel case-based reasoning subsystems called DERMA. The system has to face up several challenges that include data characterization, pattern matching, reliable diagnosis, and self-explanation capabilities. Experiments using subsystems specialized in confocal and dermoscopy images have provided promising results for helping experts to assess melanoma diagnosis. PMID:24578629

  18. Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.

    PubMed

    Wu, Yixiao; Yang, Ran; Jia, Sen; Li, Zhanjun; Zhou, Zhiyang; Lou, Ting

    2014-01-01

    This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.

  19. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    NASA Astrophysics Data System (ADS)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  20. Inherited platelet disorders: toward DNA-based diagnosis

    PubMed Central

    Lentaigne, Claire; Freson, Kathleen; Laffan, Michael A.; Turro, Ernest

    2016-01-01

    Variations in platelet number, volume, and function are largely genetically controlled, and many loci associated with platelet traits have been identified by genome-wide association studies (GWASs).1 The genome also contains a large number of rare variants, of which a tiny fraction underlies the inherited diseases of humans. Research over the last 3 decades has led to the discovery of 51 genes harboring variants responsible for inherited platelet disorders (IPDs). However, the majority of patients with an IPD still do not receive a molecular diagnosis. Alongside the scientific interest, molecular or genetic diagnosis is important for patients. There is increasing recognition that a number of IPDs are associated with severe pathologies, including an increased risk of malignancy, and a definitive diagnosis can inform prognosis and care. In this review, we give an overview of these disorders grouped according to their effect on platelet biology and their clinical characteristics. We also discuss the challenge of identifying candidate genes and causal variants therein, how IPDs have been historically diagnosed, and how this is changing with the introduction of high-throughput sequencing. Finally, we describe how integration of large genomic, epigenomic, and phenotypic datasets, including whole genome sequencing data, GWASs, epigenomic profiling, protein–protein interaction networks, and standardized clinical phenotype coding, will drive the discovery of novel mechanisms of disease in the near future to improve patient diagnosis and management. PMID:27095789

  1. Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

    PubMed Central

    Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Augelli, Raffaele; Dallaserra, Chiara; Puntel, Gino; Rossi, Arianna; Sala, Giuseppe; Signorini, Manuel; Spezia, Laura; Zamboni, Federico; Montemezzi, Stefania

    2016-01-01

    The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters’ predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization. PMID:27648166

  2. Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data

    ERIC Educational Resources Information Center

    de la Torre, Jimmy; Douglas, Jeffrey A.

    2008-01-01

    This paper studies three models for cognitive diagnosis, each illustrated with an application to fraction subtraction data. The objective of each of these models is to classify examinees according to their mastery of skills assumed to be required for fraction subtraction. We consider the DINA model, the NIDA model, and a new model that extends the…

  3. Modeling the Phenotypic Architecture of Autism Symptoms from Time of Diagnosis to Age 6

    ERIC Educational Resources Information Center

    Georgiades, Stelios; Boyle, Michael; Szatmari, Peter; Hanna, Steven; Duku, Eric; Zwaigenbaum, Lonnie; Bryson, Susan; Fombonne, Eric; Volden, Joanne; Mirenda, Pat; Smith, Isabel; Roberts, Wendy; Vaillancourt, Tracy; Waddell, Charlotte; Bennett, Teresa; Elsabbagh, Mayada; Thompson, Ann

    2014-01-01

    The latent class structure of autism symptoms from the time of diagnosis to age 6 years was examined in a sample of 280 children with autism spectrum disorder. Factor mixture modeling was performed on 26 algorithm items from the Autism Diagnostic Interview-Revised at diagnosis (Time 1) and again at age 6 (Time 2). At Time 1, a…

  4. A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

    NASA Technical Reports Server (NTRS)

    Warner, James E.; Hochhalter, Jacob D.; Leser, William P.; Leser, Patrick E.; Newman, John A

    2016-01-01

    This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

  5. Deep-reasoning fault diagnosis - An aid and a model

    NASA Technical Reports Server (NTRS)

    Yoon, Wan Chul; Hammer, John M.

    1988-01-01

    The design and evaluation are presented for the knowledge-based assistance of a human operator who must diagnose a novel fault in a dynamic, physical system. A computer aid based on a qualitative model of the system was built to help the operators overcome some of their cognitive limitations. This aid differs from most expert systems in that it operates at several levels of interaction that are believed to be more suitable for deep reasoning. Four aiding approaches, each of which provided unique information to the operator, were evaluated. The aiding features were designed to help the human's casual reasoning about the system in predicting normal system behavior (N aiding), integrating observations into actual system behavior (O aiding), finding discrepancies between the two (O-N aiding), or finding discrepancies between observed behavior and hypothetical behavior (O-HN aiding). Human diagnostic performance was found to improve by almost a factor of two with O aiding and O-N aiding.

  6. Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis

    PubMed Central

    Kim, Sewoong; Cho, Dongrae; Kim, Jihun; Kim, Manjae; Youn, Sangyeon; Jang, Jae Eun; Je, Minkyu; Lee, Dong Hun; Lee, Boreom; Farkas, Daniel L.; Hwang, Jae Youn

    2016-01-01

    We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis. PMID:28018743

  7. Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis.

    PubMed

    Kim, Sewoong; Cho, Dongrae; Kim, Jihun; Kim, Manjae; Youn, Sangyeon; Jang, Jae Eun; Je, Minkyu; Lee, Dong Hun; Lee, Boreom; Farkas, Daniel L; Hwang, Jae Youn

    2016-12-01

    We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis.

  8. Diagnosis of GLDAS LSM based aridity index and dryland identification.

    PubMed

    Ghazanfari, Sadegh; Pande, Saket; Hashemy, Mehdy; Sonneveld, Ben

    2013-04-15

    The identification of dryland areas is crucial for guiding policy aimed at intervening in water-stressed areas and addressing the perennial livelihood or food insecurity of these areas. However, the prevailing aridity indices (such as UNEP aridity index) have methodological limitations that restrict their use in delineating drylands and may be insufficient for decision-making frameworks. In this study, we propose a new aridity index based on based on 3 decades of soil moisture time series by accounting for site-specific soil and vegetation that partitions precipitation into the competing demands of evaporation and runoff. Our proposed aridity index is the frequency at which the dominant soil moisture value at a location is not exceeded by the dominant soil moisture values in all of the other locations. To represent the dominant spatial template of the soil moisture conditions, we extract the first eigenfunction from the empirical orthogonal function (EOF) analysis from 3 GLDAS land surface models (LSMs): VIC, MOSAIC and NOAH at 1 × 1 degree spatial resolution. The EOF analysis reveals that the first eigenfunction explains 33%, 43% and 47% of the VIC, NOAH and MOSAIC models, respectively. We compare each LSM aridity indices with the UNEP aridity index, which is created based on LSM data forcings. The VIC aridity index displays a pattern most closely resembling that of UNEP, although all of the LSM-based indices accurately isolate the dominant dryland areas. The UNEP classification identifies portions of south-central Africa, southeastern United States and eastern India as drier than predicted by all of the LSMs. The NOAH and MOSAIC LSMs categorize portions of southwestern Africa as drier than the other two classifications, while all of the LSMs classify portions of central India as wetter than the UNEP classification. We compare all aridity maps with the long-term average NDVI values. Results show that vegetation cover in areas that the UNEP index classifies as

  9. Intelligent-based Structural Damage Detection Model

    SciTech Connect

    Lee, Eric Wai Ming; Yu, K.F.

    2010-05-21

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  10. Kinetic analyses and performance of a colloidal magnetic nanoparticle based immunoassay dedicated to allergy diagnosis.

    PubMed

    Teste, Bruno; Kanoufi, Frédéric; Descroix, Stéphanie; Poncet, Pascal; Georgelin, Thomas; Siaugue, Jean-Michel; Petr, Jan; Varenne, Anne; Hennion, Marie-Claire

    2011-07-01

    In this paper, we demonstrate the possibility to use magnetic nanoparticles as immunosupports for allergy diagnosis. Most immunoassays used for immunosupports and clinical diagnosis are based on a heterogeneous solid-phase system and suffer from mass-transfer limitation. The nanoparticles' colloidal behavior and magnetic properties bring the advantages of homogeneous immunoassay, i.e., species diffusion, and of heterogeneous immunoassay, i.e., easy separation of the immunocomplex and free forms, as well as analyte preconcentration. We thus developed a colloidal, non-competitive, indirect immunoassay using magnetic core-shell nanoparticles (MCSNP) as immunosupports. The feasibility of such an immunoassay was first demonstrated with a model antibody and described by comparing the immunocapture kinetics using macro (standard microtiter plate), micro (microparticles) and nanosupports (MCSNP). The influence of the nanosupport properties (surface chemistry, antigen density) and of the medium (ionic strength, counter ion nature) on the immunocapture efficiency and specificity was then investigated. The performances of this original MCSNP-based immunoassay were compared with a gold standard enzyme-linked immunosorbent assay (ELISA) using a microtiter plate. The capture rate of target IgG was accelerated 200-fold and a tenfold lower limit of detection was achieved. Finally, the MCSNP-based immunoassay was successfully applied to the detection of specific IgE from milk-allergic patient's sera with a lower LOD and a good agreement (CV < 6%) with the microtiter plate, confirming the great potential of this analytical platform in the field of immunodiagnosis.

  11. Irreversible entropy model for damage diagnosis in resistors

    SciTech Connect

    Cuadras, Angel Crisóstomo, Javier; Ovejas, Victoria J.; Quilez, Marcos

    2015-10-28

    We propose a method to characterize electrical resistor damage based on entropy measurements. Irreversible entropy and the rate at which it is generated are more convenient parameters than resistance for describing damage because they are essentially positive in virtue of the second law of thermodynamics, whereas resistance may increase or decrease depending on the degradation mechanism. Commercial resistors were tested in order to characterize the damage induced by power surges. Resistors were biased with constant and pulsed voltage signals, leading to power dissipation in the range of 4–8 W, which is well above the 0.25 W nominal power to initiate failure. Entropy was inferred from the added power and temperature evolution. A model is proposed to understand the relationship among resistance, entropy, and damage. The power surge dissipates into heat (Joule effect) and damages the resistor. The results show a correlation between entropy generation rate and resistor failure. We conclude that damage can be conveniently assessed from irreversible entropy generation. Our results for resistors can be easily extrapolated to other systems or machines that can be modeled based on their resistance.

  12. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

    NASA Astrophysics Data System (ADS)

    Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Ran

    2016-05-01

    Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.

  13. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

    PubMed Central

    Cerrada, Mariela; Sánchez, René Vinicio; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-01-01

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%. PMID:26393603

  14. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal.

    PubMed

    Cerrada, Mariela; Vinicio Sánchez, René; Cabrera, Diego; Zurita, Grover; Li, Chuan

    2015-09-18

    There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

  15. Sensor-based diagnosis using knowledge of structure and function

    NASA Technical Reports Server (NTRS)

    Scarl, Ethan A.; Jamieson, John R.; Delaune, Carl I.

    1987-01-01

    A system for fault detection and isolation called LES, developed at the Kennedy Space Center for the Space Shuttle's Launch Processing System, is a well-developed diagnostic system that is simultaneously model-based and sensor-based. This experiment has led to a surprising result: the failure of a sensor can not only be handled in precisely the same way as the failure of any other object, but may present an especially easy case. Classical rule-based diagnostic systems need to find out whether or not their sensors are telling them the truth before they can safely draw inferences from them. By contrast, while LES does treat sensors as a special case, it does so only because there may exist a short cut that allows them to be handled more simply than other objects. LES uses both structural and functional knowledge, and has found cases in which the structural knowledge can be economically replaced by the judicious use of functional relationships; LES' functional relationships are stored in exactly one place, so they must be inverted to determine hypothetical values for possibly faulty objects. The inversion process has been extended to include conditional functions not normally considered to have inverses.

  16. Analyzing reliability of seizure diagnosis based on semiology.

    PubMed

    Jin, Bo; Wu, Han; Xu, Jiahui; Yan, Jianwei; Ding, Yao; Wang, Z Irene; Guo, Yi; Wang, Zhongjin; Shen, Chunhong; Chen, Zhong; Ding, Meiping; Wang, Shuang

    2014-12-01

    This study aimed to determine the accuracy of seizure diagnosis by semiological analysis and to assess the factors that affect diagnostic reliability. A total of 150 video clips of seizures from 50 patients (each with three seizures of the same type) were observed by eight epileptologists, 12 neurologists, and 20 physicians (internists). The videos included 37 series of epileptic seizures, eight series of physiologic nonepileptic events (PNEEs), and five series of psychogenic nonepileptic seizures (PNESs). After observing each video, the doctors chose the diagnosis of epileptic seizures or nonepileptic events for the patient; if the latter was chosen, they further chose the diagnosis of PNESs or PNEEs. The overall diagnostic accuracy rate for epileptic seizures and nonepileptic events increased from 0.614 to 0.660 after observations of all three seizures (p < 0.001). The diagnostic sensitivity and specificity of epileptic seizures were 0.770 and 0.808, respectively, for the epileptologists. These values were significantly higher than those for the neurologists (0.660 and 0.699) and physicians (0.588 and 0.658). A wide range of diagnostic accuracy was found across the various seizures types. An accuracy rate of 0.895 for generalized tonic-clonic seizures was the highest, followed by 0.800 for dialeptic seizures and then 0.760 for automotor seizures. The accuracy rates for myoclonic seizures (0.530), hypermotor seizures (0.481), gelastic/dacrystic seizures (0.438), and PNESs (0.430) were poor. The reliability of semiological diagnosis of seizures is greatly affected by the seizure type as well as the doctor's experience. Although the overall reliability is limited, it can be improved by observing more seizures.

  17. Design and Diagnosis Problem Solving with Multifunctional Technical Knowledge Bases

    DTIC Science & Technology

    1992-09-29

    Causal I ordering theory (Iwasaki & Simon 86) is used to reveal causal dependencies among variables in a set of equations and produce a graph...Knowledge in Device Design .......................................................... 145 QP is More Than SPQR and Dynamical Systems Theory : Response to...SThe concept of abduction has been used to frame the problem of diagnosis, scientific theory formation, natural language understanding, and is a more

  18. Bayesian network based on a fault tree and its application in diesel engine fault diagnosis

    NASA Astrophysics Data System (ADS)

    Qian, Gang; Zheng, Shengguo; Cao, Longhan

    2005-12-01

    This paper discusses the faults diagnosis of diesel engine systems. This research aims at the optimization of the diagnosis results. Inspired by Bayesian Network (BN) possessing good performance in solving uncertainty problems, a new method was proposed for establishing a BN of diesel engine faults quickly, and diagnosing faults exactly. This method consisted of two stages,namely the establishment of a BN model, and a faults diagnosis of the diesel engine system using that BN mode. For the purpose of establishing the BN, a new algorithm, which can establish a BN quickly and easily, is presented. The Fault Tree (FT) diagnosis model of the diesel engine system was established first. Then it was transformed it into a BN by using our algorithm. Finally, the BN was used to diagnose the faults of a diesel engine system. Experimental results show that the diagnosis speed is increased and the accuracy is improved.

  19. Fault Diagnosis in Discrete-Event Systems with Incomplete Models: Learnability and Diagnosability.

    PubMed

    Kwong, Raymond H; Yonge-Mallo, David L

    2015-07-01

    Most model-based approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system, or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In a previous paper, we addressed the problem of diagnosing faults given an incomplete model of the discrete-event system. We presented the learning diagnoser which not only diagnoses faults, but also attempts to learn missing model information through parsimonious hypothesis generation. In this paper, we study the properties of learnability and diagnosability. Learnability deals with the issue of whether the missing model information can be learned, while diagnosability corresponds to the ability to detect and isolate a fault after it has occurred. We provide conditions under which the learning diagnoser can learn missing model information. We define the notions of weak and strong diagnosability and also give conditions under which they hold.

  20. Convex Models of Malfunction Diagnosis in High Performance Aircraft

    DTIC Science & Technology

    1989-05-01

    initiated as in the open-loop mode: with one fixed non -zero control function. The time-dependent controller is actuated as soon as any of the state ... controllers ) the diagnosis algorithm is designed by solving 8 CONCLUI)ING REMARKS AND FUTURE RESEARCH 70 a sequence of linear optimization problems . For...Automatic Controller ............... 8 3.3 Numerical Demonstration of the Normal Dynamics ............ 8 4 Representing Control - Actuator Failure 16 5 Convex

  1. A multi-fault diagnosis method for sensor systems based on principle component analysis.

    PubMed

    Zhu, Daqi; Bai, Jie; Yang, Simon X

    2010-01-01

    A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time.

  2. A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis

    PubMed Central

    Zhu, Daqi; Bai, Jie; Yang, Simon X.

    2010-01-01

    A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time. PMID:22315537

  3. Molecular diagnosis of Chagas' disease and use of an animal model to study parasite tropism.

    PubMed

    Vera-Cruz, J M; Magallón-Gastelum, E; Grijalva, G; Rincón, A R; Ramos-García, C; Armendáriz-Borunda, J

    2003-04-01

    Chagas' disease, which is an important health problem in humans, is caused by the protozoan Trypanosoma cruzi. The cellular and molecular mechanisms, involved in the selective tropism of T. cruzi to different organs remain largely unknown. In this study we designed a PCR-based molecular diagnosis method in order to study the tropism and growth kinetics of T. cruzi in a murine model infected with parasites isolated from an endemic area of Mexico. The growth kinetics and parasite tropism of T. cruzi were also evaluated in the blood and other tissues. We observed that T. cruzi isolates from the Western Mexico showed a major tropism to mouse heart and skeletal muscles in this murine model.

  4. Actuator fault tolerant multi-controller scheme using set separation based diagnosis

    NASA Astrophysics Data System (ADS)

    Seron, María M.; De Doná, José A.

    2010-11-01

    We present a fault tolerant control strategy based on a new principle for actuator fault diagnosis. The scheme employs a standard bank of observers which match the different fault situations that can occur in the plant. Each of these observers has an associated estimation error with distinctive dynamics when an estimator matches the current fault situation of the plant. Based on the information from each observer, a fault detection and isolation (FDI) module is able to reconfigure the control loop by selecting the appropriate control law from a bank of controllers, each of them designed to stabilise and achieve reference tracking for one of the given fault models. The main contribution of this article is to propose a new FDI principle which exploits the separation of sets that characterise healthy system operation from sets that characterise transitions from healthy to faulty behaviour. The new principle allows to provide pre-checkable conditions for guaranteed fault tolerance of the overall multi-controller scheme.

  5. Model based manipulator control

    NASA Technical Reports Server (NTRS)

    Petrosky, Lyman J.; Oppenheim, Irving J.

    1989-01-01

    The feasibility of using model based control (MBC) for robotic manipulators was investigated. A double inverted pendulum system was constructed as the experimental system for a general study of dynamically stable manipulation. The original interest in dynamically stable systems was driven by the objective of high vertical reach (balancing), and the planning of inertially favorable trajectories for force and payload demands. The model-based control approach is described and the results of experimental tests are summarized. Results directly demonstrate that MBC can provide stable control at all speeds of operation and support operations requiring dynamic stability such as balancing. The application of MBC to systems with flexible links is also discussed.

  6. Tracking false-negative results in molecular diagnosis: proposal of a triplex-PCR based method for leishmaniasis diagnosis

    PubMed Central

    2014-01-01

    Background Molecular biological methods have become increasingly relevant to the diagnosis and control of infectious diseases, such as leishmaniasis. Since various factors may affect the sensitivity of PCR assays, including DNA yield and purity, an optimal extraction method is pivotal. Losses of a parasite’s DNA during extraction may significantly impair its detection by PCR and lead to false-negative results. This study proposes a triplex PCR assay targeting the parasite’s DNA, an external control (pUC18) and an internal control (G3PD) for accurate diagnosis of leishmaniasis. Results Two primer pairs were designed to detect the plasmid pUC18 and a triplex PCR assay targeting the Leishmania braziliensis kinetoplast DNA, the external control and the internal control was standardized. The triplex PCR assay was assessed for its ability to detect the three target DNA fragments simultaneously. PCR products from pUC18 DNA resulted in bands of 368 (P1) and 316 (P2) base pairs (bp). The triplex PCR optimized with the chosen external control system (P1) allowed the simultaneous detection of the internal control (G3PD – 567 bp) as well as of small quantities (10 pg) of the target parasite’s DNA, detected by amplification of a 138 bp product. Conclusions The new tool standardized herein enables a more reliable interpretation of PCR results, mainly by contributing to quality assurance of leishmaniasis diagnosis. Furthermore, after simple standardization steps, this protocol could be applied to the diagnosis of other infectious diseases in reference laboratories. This triplex PCR enables the assessment of small losses during the DNA extraction process, problems concerning DNA degradation (sample quality) and the detection of L. braziliensis kDNA. PMID:24808911

  7. Common-path and compact wavefront diagnosis system based on cross grating lateral shearing interferometer.

    PubMed

    Ling, Tong; Yang, Yongying; Yue, Xiumei; Liu, Dong; Ma, Yifang; Bai, Jian; Wang, Kaiwei

    2014-10-20

    A common-path and compact wavefront diagnosis system for both continuous and transient wavefronts measurement is proposed based on cross grating lateral shearing interferometer (CGLSI). Derived from the basic CGLSI configuration, this system employs an aplanatic lens to convert the wavefront under test into a convergent beam, which makes it possible for CGLSI to test the wavefront of collimated beams. A geometrical optics model for grating pitch determination and a Fresnel diffraction model for order selection mask design are presented. Then a detailed analysis about the influence of the grating pitch, the distance from the cross grating to the order selection mask and the numerical aperture of the aplanatic lens on the system error is made, and a calibration method is proposed to eliminate the system error. In addition, the differential Zernike polynomials fitting method is introduced for wavefront retrieval. Before our experiment, we have designed several grating pitches and their corresponding order selection mask parameters. In the final comparative experiment with ZYGO interferometer, the wavefront diagnosis system exhibits both high precision and repeatability.

  8. [On the importance of the "decision-making model" view of diagnosis as a clinical framework in psychiatry].

    PubMed

    Ota, T

    2000-01-01

    After the advent of DSM-III, operational diagnostic criteria, along with the classification of disorders using such criteria, received considerable attention, and many studies on the reliability and validity of psychiatric diagnosis were conducted worldwide. Operational methodology was applied to diagnosis and classification, especially, in the area of research, and has contributed greatly to advances in reliable and refined clinical research. Such methodology, however, has not necessarily been accepted as a guiding principle in the area of clinical practice by all psychiatrists. Rather, some psychiatrists, especially more experienced psychiatrists, took a somewhat negative attitude toward the use of operational methodology. The author contends that one of the causes for the relatively poor acceptance of operational methodology in the area of clinical practice lies in the "classification model" view of diagnosis that forms the implicit background for the methodology. From a clinical perspective, it is not from the "classification model" basis but rather, from the "decision-making model" basis that the actual process of clinical diagnosis in psychiatry is explained properly. This is a very important point, because the latter model is potentially more useful both to psychiatric patients and to researchers in psychiatry than the former model. There have been however, few reports in psychiatry that highlight the importance of this model as the clinical framework. The author analyzes the limitations of the "classification model" view, and then, based on this analysis, lists prerequisites that a model for the framework of clinical practice should possess. The prerequisites listed are: that clinical information not sufficient to meet the disease criteria should be used as effectively as possible, that diseases low in probability but high in seriousness should be considered by clinicians in the differential diagnoses; that diagnosis should be readily changed when necessary

  9. GNAR-GARCH model and its application in feature extraction for rolling bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Ma, Jiaxin; Xu, Feiyun; Huang, Kai; Huang, Ren

    2017-09-01

    Given its simplicity of modeling and sensitivity to condition variations, time series model is widely used in feature extraction to realize fault classification and diagnosis. However, nonlinear and nonstationary characteristics common in fault signals of rolling bearing bring challenges to the diagnosis. In this paper, a hybrid model, the combination of a general expression for linear and nonlinear autoregressive (GNAR) model and a generalized autoregressive conditional heteroscedasticity (GARCH) model, (i.e., GNAR-GARCH), is proposed and applied to rolling bearing fault diagnosis. An exact expression of GNAR-GARCH model is given. Maximum likelihood method is used for parameter estimation and modified Akaike Information Criterion is adopted for structure identification of GNAR-GARCH model. The main advantage of this novel model over other models is that the combination makes the model suitable for nonlinear and nonstationary signals. It is verified with statistical tests that contain comparisons among the different time series models. Finally, GNAR-GARCH model is applied to fault diagnosis by modeling mechanical vibration signals including simulation and real data. With the parameters estimated and taken as feature vectors, k-nearest neighbor algorithm is utilized to realize the classification of fault status. The results show that GNAR-GARCH model exhibits higher accuracy and better performance than do other models.

  10. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease

    NASA Astrophysics Data System (ADS)

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-07-01

    Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

  11. Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease.

    PubMed

    Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

    2016-07-01

    Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

  12. Fault diagnosis

    NASA Technical Reports Server (NTRS)

    Abbott, Kathy

    1990-01-01

    The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to

  13. Epilepsy: current evidence-based paradigms for diagnosis and treatment.

    PubMed

    Bates, Kimberly

    2015-06-01

    Although epilepsy has a prevalence of 5 to 7 per 1000 persons in the United States, it continues to be a poorly understood condition. Given the number of patients in the United States with epilepsy, it is very likely that primary care physicians will continue to provide care for these patients. This article refreshes some of the knowledge around the diagnosis of epilepsy, discusses special populations that may require additional management considerations, highlights the association of epilepsy with multiple comorbidities, and discusses antiepileptic drug (AED) treatment, including issues surrounding adherence and safety of AED therapy.

  14. Knowledge-based approach to fault diagnosis and control in distributed process environments

    NASA Astrophysics Data System (ADS)

    Chung, Kwangsue; Tou, Julius T.

    1991-03-01

    This paper presents a new design approach to knowledge-based decision support systems for fault diagnosis and control for quality assurance and productivity improvement in automated manufacturing environments. Based on the observed manifestations, the knowledge-based diagnostic system hypothesizes a set of the most plausible disorders by mimicking the reasoning process of a human diagnostician. The data integration technique is designed to generate error-free hierarchical category files. A novel approach to diagnostic problem solving has been proposed by integrating the PADIKS (Pattern-Directed Knowledge-Based System) concept and the symbolic model of diagnostic reasoning based on the categorical causal model. The combination of symbolic causal reasoning and pattern-directed reasoning produces a highly efficient diagnostic procedure and generates a more realistic expert behavior. In addition, three distinctive constraints are designed to further reduce the computational complexity and to eliminate non-plausible hypotheses involved in the multiple disorders problem. The proposed diagnostic mechanism, which consists of three different levels of reasoning operations, significantly reduces the computational complexity in the diagnostic problem with uncertainty by systematically shrinking the hypotheses space. This approach is applied to the test and inspection data collected from a PCB manufacturing operation.

  15. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    PubMed Central

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  16. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

    PubMed

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-02-21

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

  17. A GC/MS-based metabolomic approach for reliable diagnosis of phenylketonuria.

    PubMed

    Xiong, Xiyue; Sheng, Xiaoqi; Liu, Dan; Zeng, Ting; Peng, Ying; Wang, Yichao

    2015-11-01

    Although the phenylalanine/tyrosine ratio in blood has been the gold standard for diagnosis of phenylketonuria (PKU), the disadvantages of invasive sample collection and false positive error limited the application of this discriminator in the diagnosis of PKU to some extent. The aim of this study was to develop a new standard with high sensitivity and specificity in a less invasive manner for diagnosing PKU. In this study, an improved oximation-silylation method together with GC/MS was utilized to obtain the urinary metabolomic information in 47 PKU patients compared with 47 non-PKU controls. Compared with conventional oximation-silylation methods, the present approach possesses the advantages of shorter reaction time and higher reaction efficiency at a considerably lower temperature, which is beneficial to the derivatization of some thermally unstable compounds, such as phenylpyruvic acid. Ninety-seven peaks in the chromatograms were identified as endogenous metabolites by the National Institute of Standards and Technology (NIST) mass spectra library, including amino acids, organic acids, carbohydrates, amides, and fatty acids. After normalization of data using creatinine as internal standard, 19 differentially expressed compounds with p values of <0.05 were selected by independent-sample t test for the separation of the PKU group and the control group. A principal component analysis (PCA) model constructed by these differentially expressed compounds showed that the PKU group can be discriminated from the control group. Receiver-operating characteristic (ROC) analysis with area under the curve (AUC), specificity, and sensitivity of each PKU marker obtained from these differentially expressed compounds was used to evaluate the possibility of using these markers for diagnosing PKU. The largest value of AUC (0.987) with high specificity (0.936) and sensitivity (1.000) was obtained by the ROC curve of phenylacetic acid at its cutoff value (17.244 mmol/mol creatinine

  18. Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

    NASA Astrophysics Data System (ADS)

    Cong, Feiyun; Chen, Jin; Dong, Guangming; Pecht, Michael

    2013-04-01

    Rolling element bearing faults are among the main causes of breakdown in rotating machines. In this paper, a rolling bearing fault model is proposed based on the dynamic load analysis of a rotor-bearing system. The rotor impact factor is taken into consideration in the rolling bearing fault signal model. The defect load on the surface of the bearing is divided into two parts, the alternate load and the determinate load. The vibration response of the proposed fault signal model is investigated and the fault signal calculating equation is derived through dynamic and kinematic analysis. Outer race and inner race fault simulations are realized in the paper. The simulation process includes consideration of several parameters, such as the gravity of the rotor-bearing system, the imbalance of the rotor, and the location of the defect on the surface. The simulation results show that different amplitude contributions of the alternate load and determinate load will cause different envelope spectrum expressions. The rotating frequency sidebands will occur in the envelope spectrum in addition to the fault characteristic frequency. This appearance of sidebands will increase the difficulty of fault recognition in intelligent fault diagnosis. The experiments given in the paper have successfully verified the proposed signal model simulation results. The test rig design of the rotor bearing system simulated several operating conditions: (1) rotor bearing only; (2) rotor bearing with loader added; (3) rotor bearing with loader and rotor disk; and (4) bearing fault simulation without rotor influence. The results of the experiments have verified that the proposed rolling bearing signal model is important to the rolling bearing fault diagnosis of rotor-bearing systems.

  19. Fault diagnosis algorithm based on switching function for boost converters

    NASA Astrophysics Data System (ADS)

    Cho, H.-K.; Kwak, S.-S.; Lee, S.-H.

    2015-07-01

    A fault diagnosis algorithm, which is necessary for constructing a reliable power conversion system, should detect fault occurrences as soon as possible to protect the entire system from fatal damages resulting from system malfunction. In this paper, a fault diagnosis algorithm is proposed to detect open- and short-circuit faults that occur in a boost converter switch. The inductor voltage is abnormally kept at a positive DC value during a short-circuit fault in the switch or at a negative DC value during an open-circuit fault condition until the inductor current becomes zero. By employing these abnormal properties during faulty conditions, the inductor voltage is compared with the switching function to detect each fault type by generating fault alarms when a fault occurs. As a result, from the fault alarm, a decision is made in response to the fault occurrence and the fault type in less than two switching time periods using the proposed algorithm constructed in analogue circuits. In addition, the proposed algorithm has good resistivity to discontinuous current-mode operation. As a result, this algorithm features the advantages of low cost and simplicity because of its simple analogue circuit configuration.

  20. [PCR-based diagnosis of mucormycosis in tissue samples].

    PubMed

    Bialek, R; Zelck, U E

    2013-11-01

    Mucormycosis is characterized by a rapid, often fatal progression. Early diagnosis of invasive mucormycosis is the key for timely therapeutic intervention and improved survival. Contrary to the more prevalent aspergillosis, effective antifungal therapy of mucormycosis is mainly limited to amphotericin B. Given the importance to guide the timely initiation of amphotericin B and possible surgical intervention, rapid and specific identification of fungal hyphae is essential. Conventional histopathology depends on abundance and morphology of the fungi as well as on the skills of the personnel, and usually shows an accuracy of 80 %. PCR assays targeting fungal ribosomal genes to identify Mucorales at least at genus level increase sensitivity, allow a rapid identification as well as detection of double mold infections. Thus, PCR assays are beneficial to complement existing approaches. They are recommended to rapidly specify tissue diagnosis and accurate identification of fungi. This will help to guide effective therapy and thereby, survival will increase. Retrospective analyses of mucormycosis by PCR help to evaluate therapeutic interventions and will optimize treatment options.

  1. 2D nanomaterials based electrochemical biosensors for cancer diagnosis.

    PubMed

    Wang, Lu; Xiong, Qirong; Xiao, Fei; Duan, Hongwei

    2017-03-15

    Cancer is a leading cause of death in the world. Increasing evidence has demonstrated that early diagnosis holds the key towards effective treatment outcome. Cancer biomarkers are extensively used in oncology for cancer diagnosis and prognosis. Electrochemical sensors play key roles in current laboratory and clinical analysis of diverse chemical and biological targets. Recent development of functional nanomaterials offers new possibilities of improving the performance of electrochemical sensors. In particular, 2D nanomaterials have stimulated intense research due to their unique array of structural and chemical properties. The 2D materials of interest cover broadly across graphene, graphene derivatives (i.e., graphene oxide and reduced graphene oxide), and graphene-like nanomaterials (i.e., 2D layered transition metal dichalcogenides, graphite carbon nitride and boron nitride nanomaterials). In this review, we summarize recent advances in the synthesis of 2D nanomaterials and their applications in electrochemical biosensing of cancer biomarkers (nucleic acids, proteins and some small molecules), and present a personal perspective on the future direction of this area.

  2. A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment.

    PubMed

    Seixas, Flávio Luiz; Zadrozny, Bianca; Laks, Jerson; Conci, Aura; Muchaluat Saade, Débora Christina

    2014-08-01

    Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.

  3. Color model comparative analysis for breast cancer diagnosis using H and E stained images

    NASA Astrophysics Data System (ADS)

    Li, Xingyu; Plataniotis, Konstantinos N.

    2015-03-01

    Digital cancer diagnosis is a research realm where signal processing techniques are used to analyze and to classify color histopathology images. Different from grayscale image analysis of magnetic resonance imaging or X-ray, colors in histopathology images convey large amount of histological information and thus play significant role in cancer diagnosis. Though color information is widely used in histopathology works, as today, there is few study on color model selections for feature extraction in cancer diagnosis schemes. This paper addresses the problem of color space selection for digital cancer classification using H and E stained images, and investigates the effectiveness of various color models (RGB, HSV, CIE L*a*b*, and stain-dependent H and E decomposition model) in breast cancer diagnosis. Particularly, we build a diagnosis framework as a comparison benchmark and take specific concerns of medical decision systems into account in evaluation. The evaluation methodologies include feature discriminate power evaluation and final diagnosis performance comparison. Experimentation on a publicly accessible histopathology image set suggests that the H and E decomposition model outperforms other assessed color spaces. For reasons behind various performance of color spaces, our analysis via mutual information estimation demonstrates that color components in the H and E model are less dependent, and thus most feature discriminate power is collected in one channel instead of spreading out among channels in other color spaces.

  4. CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency

    PubMed Central

    2015-01-01

    Background The Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking. Objective We investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called “CIMIDx”, based on representative association rules that support the diagnosis of medical images (mammograms). Methods The proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype’s classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user’s perspective. Results We performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information

  5. Diagnosis of delay-deadline failures in real time discrete event models.

    PubMed

    Biswas, Santosh; Sarkar, Dipankar; Bhowal, Prodip; Mukhopadhyay, Siddhartha

    2007-10-01

    In this paper a method for fault detection and diagnosis (FDD) of real time systems has been developed. A modeling framework termed as real time discrete event system (RTDES) model is presented and a mechanism for FDD of the same has been developed. The use of RTDES framework for FDD is an extension of the works reported in the discrete event system (DES) literature, which are based on finite state machines (FSM). FDD of RTDES models are suited for real time systems because of their capability of representing timing faults leading to failures in terms of erroneous delays and deadlines, which FSM-based ones cannot address. The concept of measurement restriction of variables is introduced for RTDES and the consequent equivalence of states and indistinguishability of transitions have been characterized. Faults are modeled in terms of an unmeasurable condition variable in the state map. Diagnosability is defined and the procedure of constructing a diagnoser is provided. A checkable property of the diagnoser is shown to be a necessary and sufficient condition for diagnosability. The methodology is illustrated with an example of a hydraulic cylinder.

  6. Application of Three Cognitive Diagnosis Models to ESL Reading and Listening Assessments

    ERIC Educational Resources Information Center

    Lee, Yong-Won; Sawaki, Yasuyo

    2009-01-01

    The present study investigated the functioning of three psychometric models for cognitive diagnosis--the general diagnostic model, the fusion model, and latent class analysis--when applied to large-scale English as a second language listening and reading comprehension assessments. Data used in this study were scored item responses and incidence…

  7. Computer-aided diagnosis workstation for chest diagnosis based on multihelical CT images

    NASA Astrophysics Data System (ADS)

    Sato, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

    2005-04-01

    Mass screening based on 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 and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router. This electronic medical recording system and prototype internet system were developed so as not to loosen the communication among staffs of hospital. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system.

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

    NASA Astrophysics Data System (ADS)

    Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

    2007-03-01

    Multislice CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multislice 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 and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. Moreover, we have provided diagnostic assistance methods to medical screening specialists by using a lung cancer screening algorithm built into mobile helical CT scanner for the lung cancer mass screening done in the region without the hospital. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system.

  9. Malfunction diagnosis of sensors based on correlation of measurements

    NASA Astrophysics Data System (ADS)

    Lu, Wei; Teng, Jun; Wen, Runfa; Zhu, Jiayi; Li, Chao

    2017-02-01

    Structural health monitoring (SHM) is a type of on-site characterization of a real-world full-scale structure that is subjected to the real-world load cases. The fundamental element of SHM is the structural response measurements by sensors, the reliability of which is significant for safety assessment and other SHM applications. The paper proposed a method to diagnosis the fault in sensors using the correlation of measurements. The correlation of the variations of the measurements is examined using the sliding time windows, which is the principle to determine the fault in the sensors. The strain measurements from the SHM system of a real world structure, Shenzhen Bay Stadium, are performed to simulate the faults in sensors and to verify the effectiveness of the proposed method.

  10. A novel ELISA-based diagnosis of acquired von Willebrand disease with increased VWF proteolysis.

    PubMed

    Rauch, Antoine; Caron, Claudine; Vincent, Flavien; Jeanpierre, Emmanuelle; Ternisien, Catherine; Boisseau, Pierre; Zawadzki, Christophe; Fressinaud, Edith; Borel-Derlon, Annie; Hermoire, Sylvie; Paris, Camille; Lavenu-Bombled, Cécile; Veyradier, Agnès; Ung, Alexandre; Vincentelli, André; van Belle, Eric; Lenting, Peter J; Goudemand, Jenny; Susen, Sophie

    2016-05-02

    Von Willebrand disease-type 2A (VWD-2A) and acquired von Willebrand syndrome (AVWS) due to aortic stenosis (AS) or left ventricular assist device (LVAD) are associated with an increased proteolysis of von Willebrand factor (VWF). Analysis of VWF multimeric profile is the most sensitive way to assess such increased VWF-proteolysis. However, several technical aspects hamper a large diffusion among routine diagnosis laboratories. This makes early diagnosis and early appropriate care of increased proteolysis challenging. In this context of unmet medical need, we developed a new ELISA aiming a quick, easy and reliable assessment of VWF-proteolysis. This ELISA was assessed successively in a LVAD-model, healthy subjects (n=39), acquired TTP-patients (n=4), VWD-patients (including VWD-2A(IIA), n=22; VWD-2B, n=26; VWD-2A(IIE), n=21; and VWD-1C, n=8) and in AVWS-patients (AS, n=9; LVAD, n=9; and MGUS, n=8). A standard of VWF-proteolysis was specifically developed. Extent of VWF-proteolysis was expressed as relative percentage and as VWF proteolysis/VWF:Ag ratio. A speed-dependent increase in VWF-proteolysis was assessed in the LVAD model whereas no proteolysis was observed in TTP-patients. In VWD-patients, VWF-proteolysis was significantly increased in VWD-2A(IIA) and VWD-2B and significantly decreased in VWD-2A(IIE) versus controls (p< 0.0001). In AVWS-patients, VWF-proteolysis was significantly increased in AS- and LVAD-patients compared to controls (p< 0.0001) and not detectable in MGUS-patients. A significant increase in VWF-proteolysis was detected as soon as three hours after LVAD implantation (p< 0.01). In conclusion, we describe a new ELISA allowing a rapid and accurate diagnosis of VWF-proteolysis validated in three different clinical situations. This assay represents a helpful alternative to electrophoresis-based assay in the diagnosis and management of AVWS with increased VWF-proteolysis.

  11. Detection, modeling and matching of pleural thickenings from CT data towards an early diagnosis of malignant pleural mesothelioma

    NASA Astrophysics Data System (ADS)

    Chaisaowong, Kraisorn; Kraus, Thomas

    2014-03-01

    Pleural thickenings can be caused by asbestos exposure and may evolve into malignant pleural mesothelioma. While an early diagnosis plays the key role to an early treatment, and therefore helping to reduce morbidity, the growth rate of a pleural thickening can be in turn essential evidence to an early diagnosis of the pleural mesothelioma. The detection of pleural thickenings is today done by a visual inspection of CT data, which is time-consuming and underlies the physician's subjective judgment. Computer-assisted diagnosis systems to automatically assess pleural mesothelioma have been reported worldwide. But in this paper, an image analysis pipeline to automatically detect pleural thickenings and measure their volume is described. We first delineate automatically the pleural contour in the CT images. An adaptive surface-base smoothing technique is then applied to the pleural contours to identify all potential thickenings. A following tissue-specific topology-oriented detection based on a probabilistic Hounsfield Unit model of pleural plaques specify then the genuine pleural thickenings among them. The assessment of the detected pleural thickenings is based on the volumetry of the 3D model, created by mesh construction algorithm followed by Laplace-Beltrami eigenfunction expansion surface smoothing technique. Finally, the spatiotemporal matching of pleural thickenings from consecutive CT data is carried out based on the semi-automatic lung registration towards the assessment of its growth rate. With these methods, a new computer-assisted diagnosis system is presented in order to assure a precise and reproducible assessment of pleural thickenings towards the diagnosis of the pleural mesothelioma in its early stage.

  12. Waiting time disparities in breast cancer diagnosis and treatment: a population-based study in France.

    PubMed

    Molinié, F; Leux, C; Delafosse, P; Ayrault-Piault, S; Arveux, P; Woronoff, A S; Guizard, A V; Velten, M; Ganry, O; Bara, S; Daubisse-Marliac, L; Tretarre, B

    2013-10-01

    Waiting times are key indicators of a health's system performance, but are not routinely available in France. We studied waiting times for diagnosis and treatment according to patients' characteristics, tumours' characteristics and medical management options in a sample of 1494 breast cancers recorded in population-based registries. The median waiting time from the first imaging detection to the treatment initiation was 34 days. Older age, co-morbidity, smaller size of tumour, detection by organised screening, biopsy, increasing number of specimens removed, multidisciplinary consulting meetings and surgery as initial treatment were related to increased waiting times in multivariate models. Many of these factors were related to good practices guidelines. However, the strong influence of organised screening programme and the disparity of waiting times according to geographical areas were of concern. Better scheduling of diagnostic tests and treatment propositions should improve waiting times in the management of breast cancer in France.

  13. A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.

    PubMed

    Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R

    A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures.

  14. Simplified and optimized multispectral imaging for 5-ALA-based fluorescence diagnosis of malignant lesions

    PubMed Central

    Minamikawa, Takeo; Matsuo, Hisataka; Kato, Yoshiyuki; Harada, Yoshinori; Otsuji, Eigo; Yanagisawa, Akio; Tanaka, Hideo; Takamatsu, Tetsuro

    2016-01-01

    5-aminolevulinic acid (5-ALA)-based fluorescence diagnosis is now clinically applied for accurate and ultrarapid diagnosis of malignant lesions such as lymph node metastasis during surgery. 5-ALA-based diagnosis evaluates fluorescence intensity of a fluorescent metabolite of 5-ALA, protoporphyrin IX (PPIX); however, the fluorescence of PPIX is often affected by autofluorescence of tissue chromophores, such as collagen and flavins. In this study, we demonstrated PPIX fluorescence estimation with autofluorescence elimination for 5-ALA-based fluorescence diagnosis of malignant lesions by simplified and optimized multispectral imaging. We computationally optimized observation wavelength regions for the estimation of PPIX fluorescence in terms of minimizing prediction error of PPIX fluorescence intensity in the presence of typical chromophores, collagen and flavins. By using the fluorescence intensities of the optimized wavelength regions, we verified quantitative detection of PPIX fluorescence by using chemical mixtures of PPIX, flavins, and collagen. Furthermore, we demonstrated detection capability by using metastatic and non-metastatic lymph nodes of colorectal cancer patients. These results suggest the potential and usefulness of the background-free estimation method of PPIX fluorescence for 5-ALA-based fluorescence diagnosis of malignant lesions, and we expect this method to be beneficial for intraoperative and rapid cancer diagnosis. PMID:27149301

  15. Model-Based Systems

    NASA Technical Reports Server (NTRS)

    Frisch, Harold P.

    2007-01-01

    Engineers, who design systems using text specification documents, focus their work upon the completed system to meet Performance, time and budget goals. Consistency and integrity is difficult to maintain within text documents for a single complex system and more difficult to maintain as several systems are combined into higher-level systems, are maintained over decades, and evolve technically and in performance through updates. This system design approach frequently results in major changes during the system integration and test phase, and in time and budget overruns. Engineers who build system specification documents within a model-based systems environment go a step further and aggregate all of the data. They interrelate all of the data to insure consistency and integrity. After the model is constructed, the various system specification documents are prepared, all from the same database. The consistency and integrity of the model is assured, therefore the consistency and integrity of the various specification documents is insured. This article attempts to define model-based systems relative to such an environment. The intent is to expose the complexity of the enabling problem by outlining what is needed, why it is needed and how needs are being addressed by international standards writing teams.

  16. Paper-based point-of-care testing for diagnosis of dengue infections.

    PubMed

    Choi, Jane Ru; Hu, Jie; Wang, ShuQi; Yang, Hui; Wan Abas, Wan Abu Bakar; Pingguan-Murphy, Belinda; Xu, Feng

    2017-02-01

    Dengue endemic is a serious healthcare concern in tropical and subtropical countries. Although well-established laboratory tests can provide early diagnosis of acute dengue infections, access to these tests is limited in developing countries, presenting an urgent need to develop simple, rapid, and robust diagnostic tools. Point-of-care (POC) devices, particularly paper-based POC devices, are typically rapid, cost-effective and user-friendly, and they can be used as diagnostic tools for the prompt diagnosis of dengue at POC settings. Here, we review the importance of rapid dengue diagnosis, current dengue diagnostic methods, and the development of paper-based POC devices for diagnosis of dengue infections at the POC.

  17. Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps

    PubMed Central

    Zheng, Hong; Ji, Jiansong; Zhao, Liangcai; Chen, Minjiang; Shi, An; Pan, Linlin; Huang, Yiran; Zhang, Huajie; Dong, Baijun; Gao, Hongchang

    2016-01-01

    Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery. PMID:27463020

  18. Diagnosis of cerebral cryptococcoma using a computerized analysis of 1H NMR spectra in an animal model.

    PubMed

    Dzendrowskyj, Theresa E; Dolenko, Brion; Sorrell, Tania C; Somorjai, Rajmund L; Malik, Richard; Mountford, Carolyn E; Himmelreich, Uwe

    2005-06-01

    Viable cryptococci load in biopsy material from an animal model of cerebral cryptococcoma were correlated with 1H NMR spectra and metabolite profiles. A statistical classification strategy was applied to distinguish among high-resolution 1H NMR spectra acquired from cryptococcomas, glioblastomas, and normal brain tissue. The overall classification accuracy was 100% when a genetic-algorithm-based optimal region selection preceded the development of linear discriminant analysis-based classifiers. The method remained robust despite differences in the microbial load of the cryptococcoma group when harvested at different time points. These results indicate the feasibility of the method for diagnosis without isolation of the pathogenic microorganism and its potential for in vivo diagnosis based on computerized analysis of magnetic resonance spectra.

  19. A model of human decisionmaking in a fault diagnosis task

    NASA Technical Reports Server (NTRS)

    Rouse, W. B.

    1978-01-01

    Utilizing elementary concepts from the theory of fuzzy sets as well as several nonfuzzy heuristics, a model is presented of human decisionmaking in the task of troubleshooting graphically displayed networks. The performance of the model is compared to the results of two previously reported experimental studies. The ability of the model to represent human decisionmaking as a function of network size, forced-pacing, and computer aiding is considered.

  20. Using latent variable modeling and multiple imputation to calibrate rater bias in diagnosis assessment.

    PubMed

    Siddique, Juned; Crespi, Catherine M; Gibbons, Robert D; Green, Bonnie L

    2011-01-30

    We present an approach that uses latent variable modeling and multiple imputation to correct rater bias when one group of raters tends to be more lenient in assigning a diagnosis than another. Our method assumes that there exists an unobserved moderate category of patient who is assigned a positive diagnosis by one type of rater and a negative diagnosis by the other type. We present a Bayesian random effects censored ordinal probit model that allows us to calibrate the diagnoses across rater types by identifying and multiply imputing 'case' or 'non-case' status for patients in the moderate category. A Markov chain Monte Carlo algorithm is presented to estimate the posterior distribution of the model parameters and generate multiple imputations. Our method enables the calibrated diagnosis variable to be used in subsequent analyses while also preserving uncertainty in true diagnosis. We apply our model to diagnoses of posttraumatic stress disorder (PTSD) from a depression study where nurse practitioners were twice as likely as clinical psychologists to diagnose PTSD despite the fact that participants were randomly assigned to either a nurse or a psychologist. Our model appears to balance PTSD rates across raters, provides a good fit to the data, and preserves between-rater variability. After calibrating the diagnoses of PTSD across rater types, we perform an analysis looking at the effects of comorbid PTSD on changes in depression scores over time. Results are compared with an analysis that uses the original diagnoses and show that calibrating the PTSD diagnoses can yield different inferences.

  1. Model Based Definition

    NASA Technical Reports Server (NTRS)

    Rowe, Sidney E.

    2010-01-01

    In September 2007, the Engineering Directorate at the Marshall Space Flight Center (MSFC) created the Design System Focus Team (DSFT). MSFC was responsible for the in-house design and development of the Ares 1 Upper Stage and the Engineering Directorate was preparing to deploy a new electronic Configuration Management and Data Management System with the Design Data Management System (DDMS) based upon a Commercial Off The Shelf (COTS) Product Data Management (PDM) System. The DSFT was to establish standardized CAD practices and a new data life cycle for design data. Of special interest here, the design teams were to implement Model Based Definition (MBD) in support of the Upper Stage manufacturing contract. It is noted that this MBD does use partially dimensioned drawings for auxiliary information to the model. The design data lifecycle implemented several new release states to be used prior to formal release that allowed the models to move through a flow of progressive maturity. The DSFT identified some 17 Lessons Learned as outcomes of the standards development, pathfinder deployments and initial application to the Upper Stage design completion. Some of the high value examples are reviewed.

  2. Functional protease profiling for laboratory based diagnosis of invasive aspergillosis.

    PubMed

    Sabbagh, Bassel; Costina, Victor; Buchheidt, Dieter; Reinwald, Mark; Neumaier, Michael; Findeisen, Peter

    2015-07-01

    Invasive aspergillosis (IA) remains difficult to diagnose in immunocompromised patients, because diagnostic criteria according to EORTC/MSG guidelines are often not met and have low sensitivity. Hence there is an urgent need to improve diagnostic procedures by developing novel approaches. In the present study, we present a proof of concept experiment for the monitoring of Aspergillus associated protease activity in serum specimens for diagnostic purpose. Synthetic peptides that are selectively cleaved by proteases secreted from Aspergillus species were selected from our own experiments and published data. These so called reporter peptides (RP, n=5) were added to serum specimens from healthy controls (HC, n=101) and patients with proven (IA, n=9) and possible (PIA, n=144) invasive aspergillosis. Spiked samples were incubated ex vivo under strictly standardized conditions. Proteolytic fragments were analyzed using MALDI-TOF mass spectrometry. Spiked specimens of IA patients had highest concentrations of RP-fragments followed by PIA and HC. The median signal intensity was 116.546 (SD, 53.063) for IA and 5.009 (SD, 8.432) for HC. A cut-off >36.910 was chosen that performed with 100% specificity and sensitivity. Patients with PIA had either values above [53% (76/144)] or below [47% (67/144)] this chosen cut-off. The detection of respective reporter peptide fragments can easily be performed by MALDI TOF mass spectrometry. In this proof of concept study we were able to demonstrate that serum specimens of patients with IA have increased proteolytic activity towards selected reporter peptides. However, the diagnostic value of functional protease profiling has to be validated in further prospective studies. It is likely that a combination of existing and new methods will be required to achieve optimal performance for diagnosis of IA in the future.

  3. Incipient interturn fault diagnosis in induction machines using an analytic wavelet-based optimized Bayesian inference.

    PubMed

    Seshadrinath, Jeevanand; Singh, Bhim; Panigrahi, Bijaya Ketan

    2014-05-01

    Interturn fault diagnosis of induction machines has been discussed using various neural network-based techniques. The main challenge in such methods is the computational complexity due to the huge size of the network, and in pruning a large number of parameters. In this paper, a nearly shift insensitive complex wavelet-based probabilistic neural network (PNN) model, which has only a single parameter to be optimized, is proposed for interturn fault detection. The algorithm constitutes two parts and runs in an iterative way. In the first part, the PNN structure determination has been discussed, which finds out the optimum size of the network using an orthogonal least squares regression algorithm, thereby reducing its size. In the second part, a Bayesian classifier fusion has been recommended as an effective solution for deciding the machine condition. The testing accuracy, sensitivity, and specificity values are highest for the product rule-based fusion scheme, which is obtained under load, supply, and frequency variations. The point of overfitting of PNN is determined, which reduces the size, without compromising the performance. Moreover, a comparative evaluation with traditional discrete wavelet transform-based method is demonstrated for performance evaluation and to appreciate the obtained results.

  4. A Binary Programming Approach to Automated Test Assembly for Cognitive Diagnosis Models

    ERIC Educational Resources Information Center

    Finkelman, Matthew D.; Kim, Wonsuk; Roussos, Louis; Verschoor, Angela

    2010-01-01

    Automated test assembly (ATA) has been an area of prolific psychometric research. Although ATA methodology is well developed for unidimensional models, its application alongside cognitive diagnosis models (CDMs) is a burgeoning topic. Two suggested procedures for combining ATA and CDMs are to maximize the cognitive diagnostic index and to use a…

  5. Models of human problem solving - Detection, diagnosis, and compensation for system failures

    NASA Technical Reports Server (NTRS)

    Rouse, W. B.

    1983-01-01

    The role of the human operator as a problem solver in man-machine systems such as vehicles, process plants, transportation networks, etc. is considered. Problem solving is discussed in terms of detection, diagnosis, and compensation. A wide variety of models of these phases of problem solving are reviewed and specifications for an overall model outlined.

  6. Conjunctive and Disjunctive Extensions of the Least Squares Distance Model of Cognitive Diagnosis

    ERIC Educational Resources Information Center

    Dimitrov, Dimiter M.; Atanasov, Dimitar V.

    2012-01-01

    Many models of cognitive diagnosis, including the "least squares distance model" (LSDM), work under the "conjunctive" assumption that a correct item response occurs when all latent attributes required by the item are correctly performed. This article proposes a "disjunctive" version of the LSDM under which the correct item response occurs when "at…

  7. [Diagnosis "risk of pneumonia" evidence-based evaluation and comparison of nursing interventions].

    PubMed

    Haslinger-Baumann, Elisabeth; Burns, Evelin

    2007-12-01

    In home nursing care, professionally qualified nurses make decisions on their own which must be based on the latest scientific research. The goal of this systematic literature review was to examine if the interventions of the diagnosis "Risk of Pneumonia" as used by a home care service provider in Austria are based on scientific evidence. Based on the research question "Can the interventions of the diagnosis 'risk of pneumonia' be found in the scientific nursing literature?", four evidence-based guidelines from various databases and institutes were identified and evaluated using a standardized checklist. The result of the analysis is that the majority of the Nursing diagnosis interventions can be found in the guidelines. It is also recommended in the guidelines to advise and support clients to stop smoking and inoculate against influenza/pneumonia. Similarly, assessment tools should be used to estimate the severity of pneumonia. The intervention of oral hygiene as a prophylactic intervention against pneumonia should get particular attention.

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

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

  9. Modeling the Diagnosis and Treatment of Pulmonary Embolism

    ERIC Educational Resources Information Center

    Pliskin, Nava; And Others

    1978-01-01

    The problem of acute pulmonary embolism is employed to illustrate that medical decision analysis is possible despite some of the difficulties encountered in previous application. The usefulness of computerized decision models is discussed. (LBH)

  10. Application of 4G wireless network-based system for remote diagnosis and nursing of stomal complications

    PubMed Central

    Xu, Xiulian; Cao, Yingjuan; Luan, Xiaorong

    2014-01-01

    Background: This study aims to apply 4G wireless network in the remote diagnosis of stoma complications for the first time. Background: Remote diagnosis and nursing care for a variety of illnesses are urgently needed in clinical settings. Objectives: Combining with relevant clinical manifestations, an Android phone-based intelligent diagnosis system was designed to construct a universe, easy access to exploitation and human-computer interaction database and exploitation environment for applications and programs. Methods: “Production rule” and forward reasoning method were utilized to design arborescence structures and logic reasoner associated with stoma complications. Stoma physicians were responsible for delivering evaluation scores on patients’ health status using analytic hierarchy process. The emphasis of this study is to exploit an “Android phone-based system for remote diagnosis of stoma”, which is of certain universe usage. Results: Such system was tested in the Medicine Information Center of Qilu Hospital of Shandong University and initially applied in the city of De Zhou, Shandong province, China. Conclusions: These results collectively demonstrated that the system is easy to carry, of high utility and free from the limitations of wire network environment, etc. It provides clinical evidence for establishing a novel type model for the exchange between patients and physicians. PMID:25550986

  11. Traffic fatality indicators in Brazil: State diagnosis based on data envelopment analysis research.

    PubMed

    Bastos, Jorge Tiago; Shen, Yongjun; Hermans, Elke; Brijs, Tom; Wets, Geert; Ferraz, Antonio Clóvis Pinto

    2015-08-01

    The intense economic growth experienced by Brazil in recent decades and its consequent explosive motorization process have evidenced an undesirable impact: the increasing and unbroken trend in traffic fatality numbers. In order to contribute to road safety diagnosis on a national level, this study presents a research into two main indicators available in Brazil: mortality rate (represented by fatalities per capita) and fatality rate (represented by two sub-indicators, i.e., fatalities per vehicle and fatalities per vehicle kilometer traveled). These indicators were aggregated into a composite indicator or index through a multiple layer data envelopment analysis (DEA) composite indicator model, which looks for the optimum combination of indicators' weights for each decision-making unit, in this case 27 Brazilian states. The index score represents the road safety performance, based on which a ranking of states can be made. Since such a model has never been applied for road safety evaluation in Brazil, its parameters were calibrated based on the experience of more consolidated European Union research in ranking its member countries using DEA techniques. Secondly, cluster analysis was conducted aiming to provide more realistic performance comparisons and, finally, the sensitivity of the results was measured through a bootstrapping method application. It can be concluded that by combining fatality indicators, defining clusters and applying bootstrapping procedures a trustworthy ranking can be created, which is valuable for nationwide road safety planning.

  12. Improving the Performance of the Structure-Based Connectionist Network for Diagnosis of Helicopter Gearboxes

    NASA Technical Reports Server (NTRS)

    Jammu, Vinay B.; Danai, Koroush; Lewicki, David G.

    1996-01-01

    A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gear-box structure and characteristics of the 'features' of vibration to define the influences of faults on features. The 'structural influences' in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a connectionist network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal vibration features through the weights of SBCN to obtain fault possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, vibration data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the faults without training, and is able to improve its performance to nearly 100% after training.

  13. Experimental Evaluation of a Structure-Based Connectionist Network for Fault Diagnosis of Helicopter Gearboxes

    NASA Technical Reports Server (NTRS)

    Jammu, V. B.; Danai, K.; Lewicki, D. G.

    1998-01-01

    This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article. For this vibration data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated ba comparing them with the weights of a connectionist network trained though supervised learning. The results indicate general agreement between the modeled and experimentally obtained influences. The vibration data from the two gearboxes are also used to evaluate the performance of SBCN in fault diagnosis. The diagnostic results indicate that the SBCN is effective in directing the presence of faults and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate vibration features.

  14. Hyperspectral image-based analysis of weathering sensitivity for safety diagnosis of Seongsan Ilchulbong Peak

    NASA Astrophysics Data System (ADS)

    Kim, Sungho; Kim, Heekang

    2016-10-01

    This paper presents a weathering sensitivity analysis method for the safety diagnosis of Seongsan Ilchulbong Peak using hyperspectral images. Remote sensing-based safety diagnosis is important for preventing accidents in famous mountains. A hyperspectral correlation-based method is proposed to evaluate the weathering sensitivity. The three issues are how to reduce the illumination effect, how to remove camera motion while acquiring images on a boat, and how to define the weathering sensitivity index. A novel minimum subtraction and maximum normalization (MSM-norm) method is proposed to solve the shadow and specular illumination problem. Geometrically distorted hyperspectral images are corrected by estimating the borderline of the mountain and sea surface. The final issue is solved by proposing a weathering sensitivity index (WS-Index) based on a spectral angle mapper. Real experiments on the Seongsan Ilchulbong Peak (UNESCO, World Natural Heritage) highlighted the feasibility of the proposed method in safety diagnosis by the weathering sensitivity index.

  15. Diagnosis of precipitation variability in nested regional climate models

    NASA Astrophysics Data System (ADS)

    Arritt, R.; PIRCS Participants

    2003-04-01

    In order to assess reasons for model-to-model variability of precipitation in regional climate models (RCMs) we have evaluated 60-day simulations over the continental U.S. in June-July 1993 from thirteen simulations using different RCMs. The hydrologic cycles in the simulations were compared both to each other and to observations for a subregion of the upper Mississippi River Basin (UMRB), containing the region of maximum 60-day accumulated precipitation in all RCMs and station reports. All RCMs produced positive precipitation (P) minus evaporation (E) and recycling ratios that were within the range estimated from observations. RCM E was sensitive to radiation parameterization, but inter-model variability of E was spread evenly about estimates of observed E. In contrast, most RCMs produced P that was below the range of P from observations, accounting for the low values of simulated P-E compared to observations. Nine of the 13 RCMs reproduced qualitatively the observed daily cycles of P and moisture flux convergence (C), with maximum P and C occurring simultaneously at night. Three of the four driest RCMs had maximum precipitation in the afternoon, suggesting that in these RCMs afternoon destabilization by insolation had excessive influence on production of precipitation. Thus a key indicator of the ability of RCMs in this collection to properly simulate P is their ability to simulate the observed nocturnal maximum of P, indicating that the failure to resolve the diurnal cycle is closely related to overall bias in precipitation.

  16. Model compilation for embedded real-time planning and diagnosis

    NASA Technical Reports Server (NTRS)

    Barrett, Anthony

    2004-01-01

    This paper describes MEXEC, an implemented micro executive that compiles a device model into an interal structure. Not only does this structure facilitate computing the most likely current device mode from n sets of sensor measurements, but it also facilitates generating an n step reconfiguration plan that is most likely not to result in reaching a target mode - if such a plan exists.

  17. Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis.

    PubMed

    Inbarani, H Hannah; Azar, Ahmad Taher; Jothi, G

    2014-01-01

    Medical datasets are often classified by a large number of disease measurements and a relatively small number of patient records. All these measurements (features) are not important or irrelevant/noisy. These features may be especially harmful in the case of relatively small training sets, where this irrelevancy and redundancy is harder to evaluate. On the other hand, this extreme number of features carries the problem of memory usage in order to represent the dataset. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Thus, the learning model receives a concise structure without forfeiting the predictive accuracy built by using only the selected prominent features. Therefore, nowadays, FS is an essential part of knowledge discovery. In this study, new supervised feature selection methods based on hybridization of Particle Swarm Optimization (PSO), PSO based Relative Reduct (PSO-RR) and PSO based Quick Reduct (PSO-QR) are presented for the diseases diagnosis. The experimental result on several standard medical datasets proves the efficiency of the proposed technique as well as enhancements over the existing feature selection techniques.

  18. Model-based reasoning in SSF ECLSS

    NASA Technical Reports Server (NTRS)

    Miller, J. K.; Williams, George P. W., Jr.

    1992-01-01

    The interacting processes and reconfigurable subsystems of the Space Station Freedom Environmental Control and Life Support System (ECLSS) present a tremendous technical challenge to Freedom's crew and ground support. ECLSS operation and problem analysis is time-consuming for crew members and difficult for current computerized control, monitoring, and diagnostic software. These challenges can be at least partially mitigated by the use of advanced techniques such as Model-Based Reasoning (MBR). This paper will provide an overview of MBR as it is being applied to Space Station Freedom ECLSS. It will report on work being done to produce intelligent systems to help design, control, monitor, and diagnose Freedom's ECLSS. Specifically, work on predictive monitoring, diagnosability, and diagnosis, with emphasis on the automated diagnosis of the regenerative water recovery and air revitalization processes will be discussed.

  19. Validity of a Claims-Based Diagnosis of Obesity Among Medicare Beneficiaries.

    PubMed

    Lloyd, Jennifer T; Blackwell, Steve A; Wei, Iris I; Howell, Benjamin L; Shrank, William H

    2015-12-01

    Population-level data on obesity are difficult to obtain. Claims-based data sets are useful for studying public health at a population level but lack physical measurements. The objective of this study was to determine the validity of a claims-based measure of obesity compared to obesity diagnosed with clinical data as well as the validity among older adults who suffer from chronic disease. This study used data from the National Health and Nutrition Examination Survey 1999-2004 for adults aged ≥ 65 successfully linked to 1999-2007 Medicare claims (N = 3,554). Sensitivity, specificity, positive and negative predictive values, κ statistics as well as logistic regression analyses were computed for the claims-based diagnosis of obesity versus obesity diagnosed with body mass index. The claims-based diagnosis of obesity underestimates the true prevalence in the older Medicare population with a low sensitivity (18.4%). However, this method has a high specificity (97.3%) and is accurate when it is present. Sensitivity was improved when comparing the claim-based diagnosis to Class II obesity (34.2%) and when used in combination with chronic conditions such as diabetes, congestive heart failure, chronic obstructive pulmonary disease, or depression. Understanding the validity of a claims-based obesity diagnosis could aid researchers in understanding the feasibility of conducting research on obesity using claims data.

  20. Diagnosis of pneumothorax using a microwave-based detector

    NASA Astrophysics Data System (ADS)

    Ling, Geoffrey S. F.; Riechers, Ronald G., Sr.; Pasala, Krishna M.; Blanchard, Jeremy; Nozaki, Masako; Ramage, Anthony; Jackson, William; Rosner, Michael; Garcia-Pinto, Patricia; Yun, Catherine; Butler, Nathan; Riechers, Ronald G., Jr.; Williams, Daniel; Zeidman, Seth M.; Rhee, Peter; Ecklund, James M.; Fitzpatrick, Thomas; Lockhart, Stephen

    2001-08-01

    A novel method for identifying pneumothorax is presented. This method is based on a novel device that uses electromagnetic waves in the microwave radio frequency (RF) region and a modified algorithm previously used for the estimation of the angle of arrival of radar signals. In this study, we employ this radio frequency triage tool (RAFT) to the clinical condition of pneumothorax, which is a collapsed lung. In anesthetized pigs, RAFT can detect changes in the RF signature from a lung that is 20 percent or greater collapsed. These results are compared to chest x-ray. Both studies are equivalent in their ability to detect pneumothorax in pigs.

  1. Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

    NASA Astrophysics Data System (ADS)

    Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou

    2006-03-01

    Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. 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 and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.

  2. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    NASA Technical Reports Server (NTRS)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1998-01-01

    Control of air contaminants is a crucial factor in the safety considerations of crewed space flight. Indoor air quality needs to be closely monitored during long range missions such as a Mars mission, and also on large complex space structures such as the International Space Station. This work mainly pertains to the detection and simulation of air contaminants in the space station, though much of the work is easily extended to buildings, and issues of ventilation systems. Here we propose a method with which to track the presence of contaminants using an accurate physical model, and also develop a robust procedure that would raise alarms when certain tolerance levels are exceeded. A part of this research concerns the modeling of air flow inside a spacecraft, and the consequent dispersal pattern of contaminants. Our objective is to also monitor the contaminants on-line, so we develop a state estimation procedure that makes use of the measurements from a sensor system and determines an optimal estimate of the contamination in the system as a function of time and space. The real-time optimal estimates in turn are used to detect faults in the system and also offer diagnoses as to their sources. This work is concerned with the monitoring of air contaminants aboard future generation spacecraft and seeks to satisfy NASA's requirements as outlined in their Strategic Plan document (Technology Development Requirements, 1996).

  3. Bead-based microfluidic immunoassay for diagnosis of Johne's disease

    SciTech Connect

    Wadhwa, Ashutosh; Foote, Robert; Shaw, Robert W; Eda, Shigetoshi

    2012-01-01

    Microfluidics technology offers a platform for development of point-of-care diagnostic devices for various infectious diseases. In this study, we examined whether serodiagnosis of Johne s disease (JD) can be conducted in a bead-based microfluidic assay system. Magnetic micro-beads were coated with antigens of the causative agent of JD, Mycobacterium avium subsp. paratuberculosis. The antigen-coated beads were incubated with serum samples of JD-positive or negative serum samples and then with a fluorescently-labeled secondary antibody (SAB). To confirm binding of serum antibodies to the antigen, the beads were subjected to flow cytometric analysis. Different conditions (dilutions of serum and SAB, types of SAB, and types of magnetic beads) were optimized for a great degree of differentiation between the JD-negative and JD-positive samples. Using the optimized conditions, we tested a well-classified set of 155 serum samples from JD negative and JD-positive cattle by using the bead-based flow cytometric assay. Of 105 JD-positive samples, 63 samples (60%) showed higher antibody binding levels than a cut-off value determined by using antibody binding levels of JD-negative samples. In contrast, only 43-49 JD-positive samples showed higher antibody binding levels than the cut-off value when the samples were tested by commercially-available immunoassays. Microfluidic assays were performed by magnetically immobilizing a number of beads within a microchannel of a glass microchip and detecting antibody on the collected beads by laser-induced fluorescence. Antigen-coated magnetic beads treated with bovine serum sample and fluorescently-labeled SAB were loaded into a microchannel to measure the fluorescence (reflecting level of antibody binding) on the beads in the microfluidic system. When the results of five bovine serum samples obtained with the system were compared to those obtained with the flow cytometer, a high level of correlation (linear regression, r2 = 0.994) was

  4. Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter

    PubMed Central

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system’s efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  5. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    PubMed

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

  6. [Active crop canopy sensor-based nitrogen diagnosis for potato].

    PubMed

    Yu, Jing; Li, Fei; Qin, Yong-Lin; Fan, Ming-Shou

    2013-11-01

    In the present study, two potato experiments involving different N rates in 2011 were conducted in Wuchuan County and Linxi County, Inner Mongolia. Normalized difference vegetation index (NDVI) was collected by an active GreenSeeker crop canopy sensor to estimate N status of potato. The results show that the NDVI readings were poorly correlated with N nutrient indicators of potato at vegetative Growth stage due to the influence of soil background. With the advance of growth stages, NDVI values were exponentially related to plant N uptake (R2 = 0.665) before tuber bulking stage and were linearly related to plant N concentration (R2 = 0.699) when plant fully covered soil. In conclusion, GreenSeeker active crop sensor is a promising tool to estimate N status for potato plants. The findings from this study may be useful for developing N recommendation method based on active crop canopy sensor.

  7. Infrared thermography based on artificial intelligence as a screening method for carpal tunnel syndrome diagnosis.

    PubMed

    Jesensek Papez, B; Palfy, M; Mertik, M; Turk, Z

    2009-01-01

    This study further evaluated a computer-based infrared thermography (IRT) system, which employs artificial neural networks for the diagnosis of carpal tunnel syndrome (CTS) using a large database of 502 thermal images of the dorsal and palmar side of 132 healthy and 119 pathological hands. It confirmed the hypothesis that the dorsal side of the hand is of greater importance than the palmar side when diagnosing CTS thermographically. Using this method it was possible correctly to classify 72.2% of all hands (healthy and pathological) based on dorsal images and > 80% of hands when only severely affected and healthy hands were considered. Compared with the gold standard electromyographic diagnosis of CTS, IRT cannot be recommended as an adequate diagnostic tool when exact severity level diagnosis is required, however we conclude that IRT could be used as a screening tool for severe cases in populations with high ergonomic risk factors of CTS.

  8. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis.

    PubMed

    Faust, Oliver; Acharya, U Rajendra; Adeli, Hojjat; Adeli, Amir

    2015-03-01

    Electroencephalography (EEG) is an important tool for studying the human brain activity and epileptic processes in particular. EEG signals provide important information about epileptogenic networks that must be analyzed and understood before the initiation of therapeutic procedures. Very small variations in EEG signals depict a definite type of brain abnormality. The challenge is to design and develop signal processing algorithms which extract this subtle information and use it for diagnosis, monitoring and treatment of patients with epilepsy. This paper presents a review of wavelet techniques for computer-aided seizure detection and epilepsy diagnosis with an emphasis on research reported during the past decade. A multiparadigm approach based on the integration of wavelets, nonlinear dynamics and chaos theory, and neural networks advanced by Adeli and associates is the most effective method for automated EEG-based diagnosis of epilepsy.

  9. Application of PCR-based methods for diagnosis of intestinal parasitic infections in the clinical laboratory.

    PubMed

    Verweij, Jaco J

    2014-12-01

    For many years PCR- and other DNA-based methods of pathogen detection have been available in most clinical microbiology laboratories; however, until recently these tools were not routinely exploited for the diagnosis of parasitic infections. Laboratories were initially reluctant to implement PCR as incorporation of such assays within the algorithm of tools available for the most accurate diagnosis of a large variety of parasites was unclear. With regard to diagnosis of intestinal parasitic infections, the diversity of parasites that one can expect in most settings is far less than the parasitological textbooks would have you believe, hence developing a simplified diagnostic triage is feasible. Therefore the classical algorithm based on population, patient groups, use of immuno-suppressive drugs, travel history etc. is also applicable to decide when to perform and which additional techniques are to be used, if a multiplex PCR panel is used as a first-line screening diagnostic.

  10. Computed tomography-based diagnosis of diffuse compensatory enlargement of coronary arteries using scaling power laws

    PubMed Central

    Huo, Yunlong; Choy, Jenny Susana; Wischgoll, Thomas; Luo, Tong; Teague, Shawn D.; Bhatt, Deepak L.; Kassab, Ghassan S.

    2013-01-01

    Glagov's positive remodelling in the early stages of coronary atherosclerosis often results in plaque rupture and acute events. Because positive remodelling is generally diffused along the epicardial coronary arterial tree, it is difficult to diagnose non-invasively. Hence, the objective of the study is to assess the use of scaling power law for the diagnosis of positive remodelling of coronary arteries based on computed tomography (CT) images. Epicardial coronary arterial trees were reconstructed from CT scans of six Ossabaw pigs fed on a high-fat, high-cholesterol, atherogenic diet for eight months as well as the same number of body-weight-matched farm pigs fed on a lean chow (101.9±16.1 versus 91.5±13.1 kg). The high-fat diet Ossabaw pig model showed diffuse positive remodelling of epicardial coronary arteries. Good fit of measured coronary data to the length–volume scaling power law ( where Lc and Vc are crown length and volume) were found for both the high-fat and control groups (R2 = 0.95±0.04 and 0.99±0.01, respectively). The coefficient, KLV, decreased significantly in the high-fat diet group when compared with the control (14.6±2.6 versus 40.9±5.6). The flow–length scaling power law, however, was nearly unaffected by the positive remodelling. The length–volume and flow–length scaling power laws were preserved in epicardial coronary arterial trees after positive remodelling. KLV < 18 in the length–volume scaling relation is a good index of positive remodelling of coronary arteries. These findings provide a clinical rationale for simple, accurate and non-invasive diagnosis of positive remodelling of coronary arteries, using conventional CT scans. PMID:23365197

  11. Diagnosis of myocardial infarction based on lectin-induced erythrocyte agglutination: a feasibility study

    NASA Astrophysics Data System (ADS)

    Bocsi, József; Nieschke, Kathleen; Mittag, Anja; Reichert, Thomas; Laffers, Wiebke; Marecka, Monika; Pierzchalski, Arkadiusz; Piltz, Joachim; Esche, Hans-Jürgen; Wolf, Günther; Dähnert, Ingo; Baumgartner, Adolf; Tarnok, Attila

    2014-03-01

    Myocardial infarction (MI) is an acute life-threatening disease with a high incidence worldwide. Aim of this study was to test lectin-carbohydrate binding-induced red blood cell (RBC) agglutination as an innovative tool for fast, precise and cost effective diagnosis of MI. Five lectins (Ricinus communis agglutinin (RCA), Phaseolus vulgaris erythroagglutinin (PHA), Datura stramonium agglutinin (DSA), Artocarpus agglutinin (ArA), Triticum agglutinin (TA)) were tested for ability to differentiate between agglutination characteristics in patients with MI (n = 101) or angina pectoris without MI (AP) (n = 34) and healthy volunteers (HV) as control (n =68) . RBC agglutination was analyzed by light absorbance of a stirred RBC suspension in the green to red light spectrum in an agglutimeter (amtec, Leipzig, Germany) for 15 min after lectin addition. Mean cell count in aggregates was estimated from light absorbance by a mathematical model. Each lectin induced RBC agglutination. RCA led to the strongest RBC agglutination (~500 RBCs/aggregate), while the others induced substantially slower agglutination and lead to smaller aggregate sizes (5-150 RBCs/aggregate). For all analyzed lectins the lectin-induced RBC agglutination of MI or AP patients was generally higher than for HV. However, only PHA induced agglutination that clearly distinguished MI from HV. Variance analysis showed that aggregate size after 15 min. agglutination induced by PHA was significantly higher in the MI group (143 RBCs/ aggregate) than in the HV (29 RBC-s/aggregate, p = 0.000). We hypothesize that pathological changes during MI induce modification of the carbohydrate composition on the RBC membrane and thus modify RBC agglutination. Occurrence of carbohydrate-lectin binding sites on RBC membranes provides evidence about MI. Due to significant difference in the rate of agglutination between MI > HV the differentiation between these groups is possible based on PHA-induced RBC-agglutination. This novel assay

  12. Digoxin use after diagnosis of breast cancer and survival: a population-based cohort study.

    PubMed

    Karasneh, Reema A; Murray, Liam J; Mc Menamin, Úna C; Hughes, Carmel M; Cardwell, Chris R

    2015-06-01

    Digoxin has been shown to have an estrogenic effect and is associated with increased risk of gynecomastia and estrogen-sensitive cancers such as breast and uterus cancer. These findings, particularly recent observations of increased breast cancer risk, raise questions about the safety of digoxin use in breast cancer patients. Therefore, we investigated whether digoxin use after breast cancer diagnosis increased the risk of breast cancer-specific mortality in breast cancer patients. A cohort of 17,842 breast cancer patients newly diagnosed from 1998 to 2009 was identified from English cancer registries (from the National Cancer Data Repository). This cohort was linked to the UK Clinical Practice Research Datalink (to provide digoxin and other prescription records) and to the Office of National Statistics mortality data (to identify breast cancer-specific deaths). Using time-dependent Cox regression models, unadjusted and adjusted hazard ratios (HR) and 95 % confidence intervals (CIs) were calculated for the association between post-diagnostic exposure to digoxin and breast cancer-specific and all-cause mortality. In 17,842 breast cancer patients, there were 2219 breast cancer-specific deaths. Digoxin users appeared to have increased breast cancer-specific mortality compared with non-users (HR 1.73; 95 % CI 1.39-2.15) but this association was entirely attenuated after adjustment for potential confounders (adjusted HR 0.91; 95 % CI 0.72-1.14). In this large population-based breast cancer cohort study, there was little evidence of an increase in breast cancer-specific mortality with digoxin use after diagnosis. These results provide some reassurance that digoxin use is safe in breast cancer patients.

  13. AMFESYS: Modelling and diagnosis functions for operations support

    NASA Technical Reports Server (NTRS)

    Wheadon, J.

    1993-01-01

    Packetized telemetry, combined with low station coverage for close-earth satellites, may introduce new problems in presenting to the operator a clear picture of what the spacecraft is doing. A recent ESOC study has gone some way to show, by means of a practical demonstration, how the use of subsystem models combined with artificial intelligence techniques, within a real-time spacecraft control system (SCS), can help to overcome these problems. A spin-off from using these techniques can be an improvement in the reliability of the telemetry (TM) limit-checking function, as well as the telecommand verification function, of the Spacecraft Control systems (SCS). The problem and how it was addressed, including an overview of the 'AMF Expert System' prototype are described, and proposes further work which needs to be done to prove the concept. The Automatic Mirror Furnace is part of the payload of the European Retrievable Carrier (EURECA) spacecraft, which was launched in July 1992.

  14. A Physiological Signal Transmission Model to be Used for Specific Diagnosis of Cochlear Impairments

    NASA Astrophysics Data System (ADS)

    Saremi, Amin; Stenfelt, Stefan

    2011-11-01

    Many of the sophisticated characteristics of human auditory system are attributed to cochlea. Also, most of patients with a hearing loss suffer from impairments that originate from cochlea (sensorineural). Despite this, today's clinical diagnosis methods do not probe the specific origins of such cochlear lesions. The aim of this research is to introduce a physiological signal transmission model to be clinically used as a tool for diagnosis of cochlear losses. This model enables simulation of different bio-mechano-electrical processes which occur in the auditory organ of Corti inside the cochlea. What makes this model different from many available computational models is its loyalty to physiology since the ultimate goal is to model each single physiological phenomenon. This includes passive BM vibration, outer hair cells' performances such as nonlinear mechanoelectrical transduction (MET), active amplifications by somatic motor, as well as vibration to neural conversion at the inner hair cells.

  15. Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

    PubMed

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

  16. Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

    PubMed Central

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. PMID:22399894

  17. Life-Cycle-Management of CV Cables based on Degradation Diagnosis

    NASA Astrophysics Data System (ADS)

    Kato, Takeyoshi; Niwa, Mamoru; Suzuoki, Yasuo

    Life-cycle management combined with degradation diagnosis is useful for preventing an unexpected failure and extending service life of electric power apparatuses, minimizing life-cycle cost. We examined a method of life-cycle management based on TBM (Time-Based Maintenance) and CBM (Condition Based Maintenance), and evaluated the economic effect of degradation diagnosis. To carry out reliable life cycle management, however, accurate data on the relation between extent of degradation and failure probability or remaining life as well as a well-established diagnostic method are necessary. In this paper, we examine the influence of accuracy of the data used to determine the optimum diagnostic parameters and evaluate how the life-cycle cost is affected by the employment of inaccurate data. As more condition-oriented life-cycle management, we discuss the effects of a method of life-cycle management based on two diagnosis intervals in terms of reliable life-cycle management. We also discuss the measurement sensitivity necessary for an effective asset management based on diagnosis.

  18. Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Dong; Tsui, Kwok-Leung; Zhou, Qiang

    2016-05-01

    Rolling element bearings are commonly used in machines to provide support for rotating shafts. Bearing failures may cause unexpected machine breakdowns and increase economic cost. To prevent machine breakdowns and reduce unnecessary economic loss, bearing faults should be detected as early as possible. Because wavelet transform can be used to highlight impulses caused by localized bearing faults, wavelet transform has been widely investigated and proven to be one of the most effective and efficient methods for bearing fault diagnosis. In this paper, a new Gauss-Hermite integration based Bayesian inference method is proposed to estimate the posterior distribution of wavelet parameters. The innovations of this paper are illustrated as follows. Firstly, a non-linear state space model of wavelet parameters is constructed to describe the relationship between wavelet parameters and hypothetical measurements. Secondly, the joint posterior probability density function of wavelet parameters and hypothetical measurements is assumed to follow a joint Gaussian distribution so as to generate Gaussian perturbations for the state space model. Thirdly, Gauss-Hermite integration is introduced to analytically predict and update moments of the joint Gaussian distribution, from which optimal wavelet parameters are derived. At last, an optimal wavelet filtering is conducted to extract bearing fault features and thus identify localized bearing faults. Two instances are investigated to illustrate how the proposed method works. Two comparisons with the fast kurtogram are used to demonstrate that the proposed method can achieve better visual inspection performances than the fast kurtogram.

  19. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

    PubMed Central

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. PMID

  20. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

    PubMed

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

  1. A PSO-based rule extractor for medical diagnosis.

    PubMed

    Hsieh, Yi-Zeng; Su, Mu-Chun; Wang, Pa-Chun

    2014-06-01

    One of the major bottlenecks in applying conventional neural networks to the medical field is that it is very difficult to interpret, in a physically meaningful way, because the learned knowledge is numerically encoded in the trained synaptic weights. In one of our previous works, we proposed a class of Hyper-Rectangular Composite Neural Networks (HRCNNs) of which synaptic weights can be interpreted as a set of crisp If-Then rules; however, a trained HRCNN may result in some ineffective If-Then rules which can only justify very few positive examples (i.e., poor generalization). This motivated us to propose a PSO-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) which applies particle swarm optimization (PSO) to trim the rules generated by a trained HRCNN while the recognition performance will not be degraded or even be improved. The performance of the proposed PFHRCNN is demonstrated on three benchmark medical databases including liver disorders data set, the breast cancer data set and the Parkinson's disease data set.

  2. Water Diagnosis in Shrimp Aquaculture based on Neural Network

    NASA Astrophysics Data System (ADS)

    Carbajal Hernández, J. J.; Sánchez Fernández, L. P.

    2007-05-01

    In many countries, the shrimp aquaculture has not advanced computational systems to supervise the artificial habitat of the farms and laboratories. A computational system of this type helps significantly to improve the environmental conditions and to elevate the production and its quality. The main idea of this study is the creation of a system using an artificial neural network (ANN), which can help to recognize patterns of problems and their evolution in shrimp aquaculture, and thus to respond with greater rapidity against the negative effects. Bad control on the shrimp artificial habitat produces organisms with high stress and as consequence losses in their defenses. It generate low nutrition, low reproduction or worse still, they prearrange to acquire lethal diseases. The proposed system helps to control this problem. Environmental variables as pH, temperature, salinity, dissolved oxygen and turbidity have an important effect in the suitable growth of the shrimps and influence in their health. However, the exact mathematical model of this relationship is unspecified; an ANN is useful for establishing a relationship between these variables and to classify a status that describes a problem into the farm. The data classification is made to recognize and to quantify two states within the pool: a) Normal: Everything is well. b) Risk: One, some or all environmental variables are outside of the allowed interval, which generates problems. The neural network will have to recognize the state and to quantify it, in others words, how normal or risky it is, which allows finding trend of the water quality. A study was developed for designing a software tool that allows recognizing the status of the water quality and control problems for the environment into the pond.

  3. Electrochemistry-based Battery Modeling for Prognostics

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew J.; Kulkarni, Chetan Shrikant

    2013-01-01

    Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

  4. Image-based retrieval system and computer-aided diagnosis system for renal cortical scintigraphy images

    NASA Astrophysics Data System (ADS)

    Mumcuoğlu, Erkan; Nar, Fatih; Uğur, Omer; Bozkurt, M. Fani; Aslan, Mehmet

    2008-03-01

    Cortical renal (kidney) scintigraphy images are 2D images (256x256) acquired in three projection angles (posterior, right-posterior-oblique and left-posterior-oblique). These images are used by nuclear medicine specialists to examine the functional morphology of kidney parenchyma. The main visual features examined in reading the images are: size, location, shape and activity distribution (pixel intensity distribution within the boundary of each kidney). Among the above features, activity distribution (in finding scars if any) was found to have the least interobserver reproducibility. Therefore, in this study, we developed an image-based retrieval (IBR) and a computer-based diagnosis (CAD) system, focused on this feature in particular. The developed IBR and CAD algorithms start with automatic segmentation, boundary and landmark detection. Then, shape and activity distribution features are computed. Activity distribution feature is obtained using the acquired image and image set statistics of the normal patients. Active Shape Model (ASM) technique is used for more accurate kidney segmentation. In the training step of ASM, normal patient images are used. Retrieval performance is evaluated by calculating precision and recall. CAD performance is evaluated by specificity and sensitivity. To our knowledge, this paper is the first IBR or CAD system reported in the literature on renal cortical scintigraphy images.

  5. An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems

    NASA Astrophysics Data System (ADS)

    Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim

    2017-03-01

    Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.

  6. Application of simulation models to the diagnosis of MSW landfills: an example.

    PubMed

    García de Cortázar, Amaya Lobo; Tejero Monzón, Iñaki

    2007-01-01

    Among the landfill simulation programs being developed by several research groups around the world as tools for the management of sanitary landfills is MODUELO, whose second version, MODUELO 2, has been presented elsewhere. It reproduces the operational history of the landfill and its hydrologic and biodegradation processes, allowing the estimation of the flow and pollutants emitted in the leachate and the generated landfill gas over time. This program has been used for a diagnosis study of an existing European MSW landfill. The construction and calibration of the facility's hydrologic model, based on the available data, allowed the detection and quantification of two sources increasing the flows reaching the leachate collection system: a small portion (6-7%) of the runoff over the landfill surface and the contribution of water coming from external hillsides of the same watershed that represent a total surface area of around 20ha. The contrast of the leachate quality (COD, BOD, NH(4)-N and TKN) simulation results and measured data showed the potential of these models for the assessment of other significant aspects in landfill operation such as the potential harnessing of the landfill gas. Nonetheless, in this case as in many others, the accuracy of the simulation results was limited by the scant quality of the available data, which highlights the need for implementing continuous monitoring and characterizing protocols to take advantage of these programs as a tool for landfill optimization.

  7. A blood-based, 7-metabolite signature for the early diagnosis of Alzheimer's disease.

    PubMed

    Olazarán, Javier; Gil-de-Gómez, Luis; Rodríguez-Martín, Andrés; Valentí-Soler, Meritxell; Frades-Payo, Belén; Marín-Muñoz, Juan; Antúnez, Carmen; Frank-García, Ana; Acedo-Jiménez, Carmen; Morlán-Gracia, Lorenzo; Petidier-Torregrossa, Roberto; Guisasola, María Concepción; Bermejo-Pareja, Félix; Sánchez-Ferro, Álvaro; Pérez-Martínez, David A; Manzano-Palomo, Sagrario; Farquhar, Ruth; Rábano, Alberto; Calero, Miguel

    2015-01-01

    Accurate blood-based biomarkers of Alzheimer's disease (AD) could constitute simple, inexpensive, and non-invasive tools for the early diagnosis and treatment of this devastating neurodegenerative disease. We sought to develop a robust AD biomarker panel by identifying alterations in plasma metabolites that persist throughout the continuum of AD pathophysiology. Using a multicenter, cross-sectional study design, we based our analysis on metabolites whose levels were altered both in AD patients and in patients with amnestic mild cognitive impairment (aMCI), the earliest identifiable stage of AD. UPLC coupled to mass spectrometry was used to independently compare the levels of 495 plasma metabolites in aMCI (n = 58) and AD (n = 100) patients with those of normal cognition controls (NC, n = 93). Metabolite alterations common to both aMCI and AD patients were used to generate a logistic regression model that accurately distinguished AD from NC patients. The final panel consisted of seven metabolites: three amino acids (glutamic acid, alanine, and aspartic acid), one non-esterified fatty acid (22:6n-3, DHA), one bile acid (deoxycholic acid), one phosphatidylethanolamine [PE(36:4)], and one sphingomyelin [SM(39:1)]. Detailed analysis ruled out the influence of potential confounding variables, including comorbidities and treatments, on each of the seven biomarkers. The final model accurately distinguished AD from NC patients (AUC, 0.918). Importantly, the model also distinguished aMCI from NC patients (AUC, 0.826), indicating its potential diagnostic utility in early disease stages. These findings describe a sensitive biomarker panel that may facilitate the specific detection of early-stage AD through the analysis of plasma samples.

  8. Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

    ERIC Educational Resources Information Center

    Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

    2009-01-01

    Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

  9. Congenital neutropenia: diagnosis, molecular bases and patient management.

    PubMed

    Donadieu, Jean; Fenneteau, Odile; Beaupain, Blandine; Mahlaoui, Nizar; Chantelot, Christine Bellanné

    2011-05-19

    The term congenital neutropenia encompasses a family of neutropenic disorders, both permanent and intermittent, severe (<0.5 G/l) or mild (between 0.5-1.5 G/l), which may also affect other organ systems such as the pancreas, central nervous system, heart, muscle and skin. Neutropenia can lead to life-threatening pyogenic infections, acute gingivostomatitis and chronic parodontal disease, and each successive infection may leave permanent sequelae. The risk of infection is roughly inversely proportional to the circulating polymorphonuclear neutrophil count and is particularly high at counts below 0.2 G/l.When neutropenia is detected, an attempt should be made to establish the etiology, distinguishing between acquired forms (the most frequent, including post viral neutropenia and auto immune neutropenia) and congenital forms that may either be isolated or part of a complex genetic disease.Except for ethnic neutropenia, which is a frequent but mild congenital form, probably with polygenic inheritance, all other forms of congenital neutropenia are extremely rare and have monogenic inheritance, which may be X-linked or autosomal, recessive or dominant.About half the forms of congenital neutropenia with no extra-hematopoietic manifestations and normal adaptive immunity are due to neutrophil elastase (ELANE) mutations. Some patients have severe permanent neutropenia and frequent infections early in life, while others have mild intermittent neutropenia.Congenital neutropenia may also be associated with a wide range of organ dysfunctions, as for example in Shwachman-Diamond syndrome (associated with pancreatic insufficiency) and glycogen storage disease type Ib (associated with a glycogen storage syndrome). So far, the molecular bases of 12 neutropenic disorders have been identified.Treatment of severe chronic neutropenia should focus on prevention of infections. It includes antimicrobial prophylaxis, generally with trimethoprim-sulfamethoxazole, and also granulocyte

  10. Sexuality after a cancer diagnosis: A population‐based study

    PubMed Central

    Wardle, Jane; Steptoe, Andrew; Fisher, Abigail

    2016-01-01

    BACKGROUND This study explored differences in sexual activity, function, and concerns between cancer survivors and cancer‐free controls in a population‐based study. METHODS The data were from 2982 men and 3708 women who were 50 years old or older and were participating in the English Longitudinal Study of Ageing. Sexual well‐being was assessed with the Sexual Relationships and Activities Questionnaire, and cancer diagnoses were self‐reported. RESULTS There were no differences between cancer survivors and controls in levels of sexual activity (76.0% vs 78.5% for men and 58.2% vs 55.5% for women) or sexual function. Men and women with cancer diagnoses were more dissatisfied with their sex lives than controls (age‐adjusted percentages: 30.9% vs 19.8% for men [P = .023] and 18.2% vs 11.8% for women [P = .034]), and women with cancer were more concerned about levels of sexual desire (10.2% vs 7.1%; P = .006). Women diagnosed < 5 years ago were more likely to report difficulty with becoming aroused (55.4% vs 31.8%; P = .016) and achieving orgasm (60.6% vs 28.3%; P < .001) and were more concerned about sexual desire (14.8% vs 7.1%; P = .007) and orgasmic experience (17.6% vs 7.1%; P = .042) than controls, but there were no differences in men. CONCLUSIONS Self‐reports of sexual activity and functioning in older people with cancer are broadly comparable to age‐matched, cancer‐free controls. There is a need to identify the causes of sexual dissatisfaction among long‐term cancer survivors despite apparently normal levels of sexual activity and function for their age. The development of interventions addressing low sexual desire and problems with sexual functioning in women is also important and may be particularly relevant for cancer survivors after treatment. Cancer 2016;122:3883–3891. © 2016 American Cancer Society. PMID:27531631

  11. Relations among system diagnosis models with three-valued test outcomes

    SciTech Connect

    Butler, J.T.

    1983-01-01

    Three models of multiprocessing systems are compared on the basis of the accuracy of the diagnosis of faulty processors. One model is the conventional system with binary-valued test outcomes (pass and fail). The other models have three-valued test outcomes, where the third value is either a missing or an incorrect test result. It is shown that, in general multiprocessing systems, there is a hierarchy among the three models with respect to diagnosability. However, in systems where no two processors test each other, the models are on par, and established criteria for diagnosability in binary systems can be used in both of the three-valued systems. 12 references.

  12. MYCIN: A Knowledge-Based Consultation Program for Infectious Disease Diagnosis.

    ERIC Educational Resources Information Center

    van Melle, William

    1978-01-01

    Describes the structure of MYCIN, a computer-based consultation system designed to assist physicians in the diagnosis of and therapy selection for patients with bacterial infections, and an explanation system which can answer simple English questions in order to justify its advice or educate the user. (Author/VT)

  13. Investigation of candidate data structures and search algorithms to support a knowledge based fault diagnosis system

    NASA Technical Reports Server (NTRS)

    Bosworth, Edward L., Jr.

    1987-01-01

    The focus of this research is the investigation of data structures and associated search algorithms for automated fault diagnosis of complex systems such as the Hubble Space Telescope. Such data structures and algorithms will form the basis of a more sophisticated Knowledge Based Fault Diagnosis System. As a part of the research, several prototypes were written in VAXLISP and implemented on one of the VAX-11/780's at the Marshall Space Flight Center. This report describes and gives the rationale for both the data structures and algorithms selected. A brief discussion of a user interface is also included.

  14. Abnormal placentation: evidence-based diagnosis and management of placenta previa, placenta accreta, and vasa previa.

    PubMed

    Rao, Kiran Prabhaker; Belogolovkin, Victoria; Yankowitz, Jerome; Spinnato, Joseph A

    2012-08-01

    Placenta previa, placenta accreta, and vasa previa cause significant maternal and perinatal morbidity and mortality. With the increasing incidence of both cesarean delivery and pregnancies using assisted reproductive technology, these 3 conditions are becoming more common. Advances in grayscale and Doppler ultrasound have facilitated prenatal diagnosis of abnormal placentation to allow the development of multidisciplinary management plans to achieve the best outcomes for mother and baby. We present a comprehensive review of the literature on abnormal placentation including an evidence-based approach to diagnosis and management.

  15. Anatomic diagnosis of congenital heart disease. A practical approach based on the sequentiality principle.

    PubMed

    Cervantes-Salazar, Jorge; Curi-Curi, Pedro; Ramírez-Marroquín, Samuel; Calderón-Colmenero, Juan; Munoz-Castellanos, Luis

    2010-01-01

    Based on the sequentiality principle, this review proposes a practical method that allows the systematization of the anatomic diagnosis of congenital heart disease. We emphasize the need to use sequential connection between the different cardiac segments: atria, ventricles and great arteries. Five ordered steps are defined, which include determination of atrial situs and of the connection features between the ventricles and the great arteries. Related lesions and some additional special features are a second stage in the sequential analysis of congenital heart disease, which is also important for the integral diagnosis.

  16. Early diagnosis of Lemierre's syndrome based on a medical history and physical findings.

    PubMed

    Murata, Yutaka; Wada, Mikio; Kawashima, Atsushi; Kagawa, Keizo

    2013-01-01

    A 37-year-old woman presented with fever and rigor after experiencing respiratory symptoms the previous week that had resolved within a few days. On presentation, her neck was swollen along the right sternocleidomastoid muscle, and chest CT showed pulmonary septic embolisms. Lemierre's syndrome was strongly suspected based on the patient's medical history and physical findings. Further examination revealed venous thrombus, and Fusobacterium necrophorum was later isolated from blood cultures. Antibiotics for anaerobes were administered before a final diagnosis was made, and the patient's symptoms thereafter improved. A rapid diagnosis is essential, since Lemierre's syndrome can be fatal with a diagnostic delay.

  17. Technical advances in flow cytometry-based diagnosis and monitoring of paroxysmal nocturnal hemoglobinuria

    PubMed Central

    Correia, Rodolfo Patussi; Bento, Laiz Cameirão; Bortolucci, Ana Carolina Apelle; Alexandre, Anderson Marega; Vaz, Andressa da Costa; Schimidell, Daniela; Pedro, Eduardo de Carvalho; Perin, Fabricio Simões; Nozawa, Sonia Tsukasa; Mendes, Cláudio Ernesto Albers; Barroso, Rodrigo de Souza; Bacal, Nydia Strachman

    2016-01-01

    ABSTRACT Objective: To discuss the implementation of technical advances in laboratory diagnosis and monitoring of paroxysmal nocturnal hemoglobinuria for validation of high-sensitivity flow cytometry protocols. Methods: A retrospective study based on analysis of laboratory data from 745 patient samples submitted to flow cytometry for diagnosis and/or monitoring of paroxysmal nocturnal hemoglobinuria. Results: Implementation of technical advances reduced test costs and improved flow cytometry resolution for paroxysmal nocturnal hemoglobinuria clone detection. Conclusion: High-sensitivity flow cytometry allowed more sensitive determination of paroxysmal nocturnal hemoglobinuria clone type and size, particularly in samples with small clones. PMID:27759825

  18. Hybrid Modeling and Diagnosis in the Real World: A Case Study

    DTIC Science & Technology

    2002-05-04

    mapped to system components case study of an aircraft fuel system, and discuss and parameters. The relations in the model are employed to methodologies for...UNCLASSIFIED Defense Technical Information Center Compilation Part Notice ADP012687 TITLE: Hybrid Modeling and Diagnosis in the Real World : A Case...Study DISTRIBUTION: Approved for public release, distribution unlimited This paper is part of the following report: TITLE: Thirteenth International

  19. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network.

    PubMed

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-10-13

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

  20. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

    PubMed Central

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-01-01

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods. PMID:27754386

  1. Rapid immunohistochemistry based on alternating current electric field for intraoperative diagnosis of brain tumors.

    PubMed

    Tanino, Mishie; Sasajima, Toshio; Nanjo, Hiroshi; Akesaka, Shiori; Kagaya, Masami; Kimura, Taichi; Ishida, Yusuke; Oda, Masaya; Takahashi, Masataka; Sugawara, Taku; Yoshioka, Toshiaki; Nishihara, Hiroshi; Akagami, Yoichi; Goto, Akiteru; Minamiya, Yoshihiro; Tanaka, Shinya

    2015-01-01

    Rapid immunohistochemistry (R-IHC) can contribute to the intraoperative diagnosis of central nervous system (CNS) tumors. We have recently developed a new IHC method based on an alternating current electric field to facilitate the antigen-antibody reaction. To ensure the requirement of R-IHC for intraoperative diagnosis, 183 cases of CNS tumors were reviewed regarding the accuracy rate of diagnosis without R-IHC. The diagnostic accuracy was 90.7 % (166/183 cases) [corrected] in which definitive diagnoses were not provided in 17 cases because of the failure of glioma grading and differential diagnosis of lymphoma and glioma. To establish the clinicopathological application, R-IHC for frozen specimens was compared with standard IHC for permanent specimens. 33 gliomas were analyzed, and the Ki-67/MIB-1 indices of frozen specimens by R-IHC were consistent with the grade and statistically correlated with those of permanent specimens. Thus, R-IHC provided supportive information to determine the grade of glioma. For discrimination between glioma and lymphoma, R-IHC was able to provide clear results of CD20 and Ki-67/MIB-1 in four frozen specimens of CNS lymphoma as well as standard IHC. We conclude that the R-IHC for frozen specimens can provide important information for intraoperative diagnosis of CNS tumors.

  2. Late diagnosis of congenital toxoplasmosis based on serological follow-up: A case report.

    PubMed

    Dard, Céline; Chemla, Cathy; Fricker-Hidalgo, Hélène; Brenier-Pinchart, Marie-Pierre; Baret, Marie; Mzabi, Alexandre; Villena, Isabelle; Pelloux, Hervé

    2017-04-01

    Toxoplasma gondii is a protozoan parasite infecting up to one third of the world's population. T. gondii infection is usually benign in immunocompetent patients but can be life-threatening when congenitally transmitted. Congenital toxoplasmosis presentation ranges from severe central nervous system and ocular features, to a well appearing newborn with onset of complications late in childhood. The diagnosis of subclinical form remains important since early treatment reduces later complications such as chorioretinitis. We report an atypical case of congenital toxoplasmosis with a delayed diagnosis, based on Toxoplasma-specific serological follow-up. The infant was born to a mother who became infected during pregnancy, thus inducing infant biological and clinical follow-up. Neither biological nor clinical arguments favored a diagnosis of congenital toxoplasmosis until ten months of life. Congenital toxoplasmosis was then suspected because of an unusual increase of specific IgG levels. Diagnosis was confirmed by detection of newly synthesized newborn Ig isotypes using complementary comparative mother-to-child immunological profile techniques and specific treatment therefore administered. This report highlights the importance to follow up newborns at risk of congenital toxoplasmosis with specific and newborn-appropriate techniques until Toxoplasma-IgG titers are completely negative. This allows not only the exclusion of congenital toxoplasmosis when serology becomes negative, but also the diagnosis and treatment of congenital toxoplasmosis when infection is detected later in development.

  3. A fault diagnosis system for PV power station based on global partitioned gradually approximation method

    NASA Astrophysics Data System (ADS)

    Wang, S.; Zhang, X. N.; Gao, D. D.; Liu, H. X.; Ye, J.; Li, L. R.

    2016-08-01

    As the solar photovoltaic (PV) power is applied extensively, more attentions are paid to the maintenance and fault diagnosis of PV power plants. Based on analysis of the structure of PV power station, the global partitioned gradually approximation method is proposed as a fault diagnosis algorithm to determine and locate the fault of PV panels. The PV array is divided into 16x16 blocks and numbered. On the basis of modularly processing of the PV array, the current values of each block are analyzed. The mean current value of each block is used for calculating the fault weigh factor. The fault threshold is defined to determine the fault, and the shade is considered to reduce the probability of misjudgments. A fault diagnosis system is designed and implemented with LabVIEW. And it has some functions including the data realtime display, online check, statistics, real-time prediction and fault diagnosis. Through the data from PV plants, the algorithm is verified. The results show that the fault diagnosis results are accurate, and the system works well. The validity and the possibility of the system are verified by the results as well. The developed system will be benefit for the maintenance and management of large scale PV array.

  4. Sequential Fuzzy Diagnosis Method for Motor Roller Bearing in Variable Operating Conditions Based on Vibration Analysis

    PubMed Central

    Li, Ke; Ping, Xueliang; Wang, Huaqing; Chen, Peng; Cao, Yi

    2013-01-01

    A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are used for extracting the feature spectra from the non-stationary vibration signal measured for condition diagnosis. The RCI is used to automatically extract the feature spectrum from the time-frequency distribution of the vibration signal. The extracted feature spectrum is instantaneous, and not correlated with the rotation speed and load. By using the ant colony optimization (ACO) clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. The experimental results shows that the diagnostic sensitivity of the SSP is higher than original symptom parameter (SP), and the SSP can sensitively reflect the characteristics of the feature spectrum for precise condition diagnosis. Finally, a fuzzy diagnosis method based on sequential inference and possibility theory is also proposed, by which the conditions of the machine can be identified sequentially as well. PMID:23793021

  5. Attention Deficit/Hyperactivity Disorder (ADHD) Diagnosis: An Activation-Executive Model

    PubMed Central

    Rodríguez, Celestino; González-Castro, Paloma; Cueli, Marisol; Areces, Debora; González-Pienda, Julio A.

    2016-01-01

    Attention deficit with, or without, hyperactivity and impulsivity (ADHD) is categorized as neuro-developmental disorder. ADHD is a common disorder in childhood and one of the most frequent conditions affecting school ages. This disorder is characterized by a persistent behavioral pattern associated with inattention, over-activity (or hyperactivity), and difficulty in controlling impulses. Current research suggests the existence of certain patterns of cortical activation and executive control, which could more objectively identify ADHD. Through the use of a risk and resilience model, this research aimed to analyze the interaction between brain activation variables (nirHEG and Q-EEG) and executive variables (Continuous performance test -CPT-) in subjects with ADHD. The study involved 499 children, 175 females (35.1%) and 324 males (64.91%); aged from 6 to 16 years (M = 11.22, SD = 1.43). Two hundred and fifty six of the children had been diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and 243 were without ADHD. For the analysis of this objective, a causal model was designed to include the following different measures of task-execution: CPT TOVA (omissions, commissions, response time, variability, D prime and the ADHD Index); electrical activity (using Q-EEG); and blood-flow oxygenation activity (using nirHEG). The causal model was tested by means of structural equation modeling (SEM). The model that had been constructed was based upon three general assumptions: (1) There are different causal models for children with ADHD and those without ADHD; (2) The activation measures influence students’ executive performance; and (3) There are measurable structural differences between the ADHD and control group models (executive and activation). In general, the results showed that: (a) activation measures influence executive patterns differently, (b) the relationship between activation variables (nirHEG and Q-EEG) depends on the brain zone being studied, (c

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-08

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

  8. Kawasaki disease: an evidence based approach to diagnosis, treatment, and proposals for future research

    PubMed Central

    Brogan, P; Bose, A; Burgner, D; Shingadia, D; Tulloh, R; Michie, C; Klein, N; Booy, R; Levin, M; Dillon, M

    2002-01-01

    This article proposes a clinical guideline for the diagnosis and treatment of Kawasaki disease in the UK based on the best available evidence to date, and highlights areas of practice where evidence is anecdotal or based on retrospective data. Future research as proposed by the London Kawasaki Disease Research Group is outlined, and clinicians are invited to prospectively enrol their suspected cases into this collaborative research project. PMID:11919108

  9. Online motor fault detection and diagnosis using a hybrid FMM-CART model.

    PubMed

    Seera, Manjeevan; Lim, Chee Peng

    2014-04-01

    In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

  10. Non-invasive diagnosis of papillary thyroid microcarcinoma: a NMR-based metabolomics approach

    PubMed Central

    Lu, Jinghui; Hu, Sanyuan; Miccoli, Paolo; Zeng, Qingdong; Liu, Shaozhuang; Ran, Lin; Hu, Chunxiao

    2016-01-01

    Papillary thyroid microcarcinoma (PTMC) is a subtype of papillary thyroid carcinoma (PTC). Because its diameter is less than 10 mm, diagnosing it accurately is difficult with traditional methods such as image examinations and FNA (Fine Needle Aspiration). Investigating the metabolic changes induced by PTMC may enhance the understanding of its pathogenesis and provide important information for a new diagnosis method and treatment plan. In this study, high resolution magic angle spin (HRMAS) spectroscopy and 1H-nuclear magnetic resonance (1H-NMR) spectroscopy were used to screen metabolic changes in thyroid tissues and plasma from PTMC patients respectively. The results revealed reduced levels of fatty acids and elevated levels of several amino acids (phenylalanine, tyrosine, lactate, serine, cystine, lysine, glutamine/glutamate, taurine, leucine, alanine, isoleucine and valine) in thyroid tissues, as well as reduced levels of amino acids such as valine, tyrosine, proline, lysine, leucine and elevated levels of glucose, mannose, pyruvate and 3-hydroxybutyrate in plasma, are involved in the metabolic alterations in PTMC. In addition, a receiver operating characteristic (ROC) curve model for PTMC prediction was able to classify cases with good sensitivity and specificity using 9 significant changed metabolites in plasma. This work illustrates that the NMR-based metabolomics approach is capable of providing more sensitive diagnostic results and more systematic therapeutic information for PTMC. PMID:27835583

  11. Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis

    PubMed Central

    Ortiz, Andrés; Munilla, Jorge; Álvarez-Illán, Ignacio; Górriz, Juan M.; Ramírez, Javier

    2015-01-01

    Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the

  12. A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis.

    PubMed

    Akutekwe, Arinze; Seker, Huseyin

    2014-01-01

    Computational and machine learning techniques have been applied in identifying biomarkers and constructing predictive models for diagnosis of hypertension. Strategies such as improved classification rules based on decision trees have been proposed. Other techniques such as Fuzzy Expert Systems (FES) and Neuro-Fuzzy Systems (NFS) have recently been applied. However, these methods lack the ability to detect temporal relationships among biomarker genes that will aid better understanding of the mechanism of hypertension disease. In this paper we apply a proposed two-stage bio-network construction approach that combines the power and computational efficiency of classification methods with the well-established predictive ability of Dynamic Bayesian Network. We demonstrate our method using the analysis of male young-onset hypertension microarray dataset. Four key genes were identified by the Least Angle Shrinkage and Selection Operator (LASSO) and three Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods. Results show that cell regulation FOXQ1 may inhibit the expression of focusyltransferase-6 (FUT6) and that ABCG1 ATP-binding cassette sub-family G may also play inhibitory role against NR2E3 nuclear receptor sub-family 2 and CGB2 Chromatin Gonadotrophin.

  13. Model-based software design

    NASA Technical Reports Server (NTRS)

    Iscoe, Neil; Liu, Zheng-Yang; Feng, Guohui; Yenne, Britt; Vansickle, Larry; Ballantyne, Michael

    1992-01-01

    Domain-specific knowledge is required to create specifications, generate code, and understand existing systems. Our approach to automating software design is based on instantiating an application domain model with industry-specific knowledge and then using that model to achieve the operational goals of specification elicitation and verification, reverse engineering, and code generation. Although many different specification models can be created from any particular domain model, each specification model is consistent and correct with respect to the domain model.

  14. An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method.

    PubMed

    Lu, Chunhong; Zhu, Zhaomin; Gu, Xiaofeng

    2014-09-01

    In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis.

  15. Surface plasmon resonance based biosensor: A new platform for rapid diagnosis of livestock diseases

    PubMed Central

    Sahoo, Pravas Ranjan; Swain, Parthasarathi; Nayak, Sudhanshu Mohan; Bag, Sudam; Mishra, Smruti Ranjan

    2016-01-01

    Surface plasmon resonance (SPR) based biosensors are the most advanced and developed optical label-free biosensor technique used for powerful detection with vast applications in environmental protection, biotechnology, medical diagnostics, drug screening, food safety, and security as well in livestock sector. The livestock sector which contributes the largest economy of India, harbors many bacterial, viral, and fungal diseases impacting a great loss to the production and productive potential which is a major concern in both small and large ruminants. Hence, an accurate, sensitive, and rapid diagnosis is required for prevention of these above-mentioned diseases. SPR based biosensor assay may fulfill the above characteristics which lead to a greater platform for rapid diagnosis of different livestock diseases. Hence, this review may give a detail idea about the principle, recent development of SPR based biosensor techniques and its application in livestock sector. PMID:28096602

  16. A Knowledge-Based System for the Computer Assisted Diagnosis of Endoscopic Images

    NASA Astrophysics Data System (ADS)

    Kage, Andreas; Münzenmayer, Christian; Wittenberg, Thomas

    Due to the actual demographic development the use of Computer-Assisted Diagnosis (CAD) systems becomes a more important part of clinical workflows and clinical decision making. Because changes on the mucosa of the esophagus can indicate the first stage of cancerous developments, there is a large interest to detect and correctly diagnose any such lesion. We present a knowledge-based system which is able to support a physician with the interpretation and diagnosis of endoscopic images of the esophagus. Our system is designed to support the physician directly during the examination of the patient, thus prodving diagnostic assistence at the point of care (POC). Based on an interactively marked region in an endoscopic image of interest, the system provides a diagnostic suggestion, based on an annotated reference image database. Furthermore, using relevant feedback mechanisms, the results can be enhanced interactively.

  17. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    PubMed

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method.

  18. Model-Based Reasoning

    ERIC Educational Resources Information Center

    Ifenthaler, Dirk; Seel, Norbert M.

    2013-01-01

    In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper…

  19. Principles of models based engineering

    SciTech Connect

    Dolin, R.M.; Hefele, J.

    1996-11-01

    This report describes a Models Based Engineering (MBE) philosophy and implementation strategy that has been developed at Los Alamos National Laboratory`s Center for Advanced Engineering Technology. A major theme in this discussion is that models based engineering is an information management technology enabling the development of information driven engineering. Unlike other information management technologies, models based engineering encompasses the breadth of engineering information, from design intent through product definition to consumer application.

  20. Automatic differential diagnosis of pancreatic serous and mucinous cystadenomas based on morphological features.

    PubMed

    Song, Jae-Won; Lee, Ju-Hong; Choi, Joon-Hyuk; Chun, Seok-Ju

    2013-01-01

    Generally, pathological diagnosis using an electron microscope is time-consuming and likely to result in a subjective judgment, because pathologists perform manual screening of tissue slides at high magnifications. Recently, the advent of digital pathology technology has provided the basis for convenient screening and quantitative analysis by digitizing tissue slides through a computer system. However, a screening process with high magnification still takes quite a long time. To solve these problems, recently the use of computer-aided design techniques for performing pathologic diagnosis has been increasing in digital pathology. For pathological diagnosis, we need different diagnostic methods for different regions with different characteristics. Therefore, in order to effectively diagnose different lesions and types of diseases, a quantitative method for extracting specific features is required in computerized pathologic diagnosis. This study is about an automated differential diagnosis system to differentiate between benign serous cystadenoma and possibly-malignant mucinous cystadenoma. In order to diagnose cystic tumors, the first step is identifying a cystic region and inspecting its epithelial cells. First, we identify the lumen boundary of a cyst using the Direction Cumulative Map considering 8-ways. Then, the Epithelial Nuclei Identification algorithm is used to discern epithelial nuclei. After that, three morphological features for the differential diagnosis of mucinous and serous cystadenomas are extracted. To demonstrate the superiority of the proposed features, the experiments compared performance of the classifiers learned by using the proposed morphological features and the classical morphological features based on nuclei. The classifiers in the simulations are as follows; Bayesian Classifier, k-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. The results show that all classifiers using the proposed features have the best

  1. Disposable dry-reagent cotton thread-based point-of-care diagnosis devices for protein and nucleic acid test.

    PubMed

    Mao, Xun; Du, Ting-E; Wang, Yiyun; Meng, Lili

    2015-03-15

    We report here for the first time by using dry-reagent cotton thread-based point-of-care diagnosis devices for low-cost, sensitive and rapid detection of a lung cancer related biomarker, squamous cell carcinoma antigen (SCCA) and a human genetic disease, hereditary tyrosinemia type I related DNA sequences. A model system comprising SCCA as an analyte and a pair of monoclonal antibodies is used to demonstrate the proof-of-concept on the dry-reagent cotton thread based immunoassay device. An enhancement protocol was employed by using two kinds of gold nanoparticle labels for SCCA test which greatly improved the sensitivity of the device. The assay avoids the multiple incubation and washing steps performed in most conventional protein analyses, which is similar with the lateral flow strip technology. Under optimal conditions, the thread based immunoassay device was capable of measuring 1ng/mL SCCA in 20min which meet the requirement for clinical diagnosis. DNA detection was successfully realized by using a novel adenosine based molecular beacon probe as reporter probes in the cotton thread based device, the linear range is 75-3000fmol which is suitable for quantitative test.

  2. Multi-period medical diagnosis method using a single valued neutrosophic similarity measure based on tangent function.

    PubMed

    Ye, Jun; Fu, Jing

    2016-01-01

    Because of the increased volume of information available to physicians from advanced medical technology, the obtained information of each symptom with respect to a disease may contain truth, falsity and indeterminacy information. Since a single-valued neutrosophic set (SVNS) consists of the three terms like the truth-membership, indeterminacy-membership and falsity-membership functions, it is very suitable for representing indeterminate and inconsistent information. Then, similarity measure plays an important role in pattern recognition and medical diagnosis. However, existing medical diagnosis methods can only handle the single period medical diagnosis problem, but cannot deal with the multi-period medical diagnosis problems with neutrosophic information. Hence, the purpose of this paper was to propose similarity measures between SVNSs based on tangent function and a multi-period medical diagnosis method based on the similarity measure and the weighted aggregation of multi-period information to solve multi-period medical diagnosis problems with single-valued neutrosophic information. Then, we compared the tangent similarity measures of SVNSs with existing similarity measures of SVNSs by a numerical example about pattern recognitions to indicate the effectiveness and rationality of the proposed similarity measures. In the multi-period medical diagnosis method, we can find a proper diagnosis for a patient by the proposed similarity measure between the symptoms and the considered diseases represented by SVNSs and the weighted aggregation of multi-period information. Then, a multi-period medical diagnosis example was presented to demonstrate the application of the proposed diagnosis method and to indicate the effectiveness of the proposed diagnosis method by the comparative analysis. The diagnosis results showed that the developed multi-period medical diagnosis method can help doctors make a proper diagnosis by the comprehensive information of multi-periods.

  3. [Implementation of the unified model of presenting cancer diagnosis in Polish pediatric onco-hematology centers].

    PubMed

    Samardakiewicz, Marzena; Kowalczyk, Jerzy R; Mazurowa, Mieczysława; Budziński, Waldemar; Antonowicz, Małgorzata; Borysławska, Anna; Szweda, Elzbieta; Groth, Anna; Pyka, Małgorzata; Figurska, Martyna; Chybicka, Alicja; Rokicka-Milewska, Roma; Matysiak, Michał; Balwierz, Walentyna; Sońta-Jakimczyk, Danuta; Balcerska, Anna; Wachowiak, Jacek

    2004-01-01

    Presentation of full information related to diagnosis of children with cancer should be one of principles in pediatric oncology. Multidisciplinary approach to each newly diagnosed child and its parents contributes to improving this standard. The Polish Pediatric Leukemia and Lymphoma Group is engaged in these activities since 1998 and it resulted in implementation of several SIOP recommendations in most of Polish pediatric oncohematology centers. The unified model of presentation of diagnosis for a child, parents and family was of an importance and the efforts to introduce it in all cooperating centers was undertaken. Proposed model of informing consists of several steps. Procedure should be individually tailored according to natural history of the disease and characteristics of the family. The purpose of the study was to evaluate the informing procedure in 60 children with newly diagnosed neoplasmatic disease.

  4. Sporadic colon cancer murine models demonstrate the value of autoantibody detection for preclinical cancer diagnosis.

    PubMed

    Barderas, Rodrigo; Villar-Vázquez, Roi; Fernández-Aceñero, María Jesús; Babel, Ingrid; Peláez-García, Alberto; Torres, Sofía; Casal, J Ignacio

    2013-10-15

    Although autoantibody detection has been proposed for diagnosis of colorectal cancer, little is known about their initial production and development correlation with cancer progression. Azoxymethane/dextran sodium sulfate (AOM/DSS)-treated mice developed colon adenocarcinoma in the distal colon similar to human sporadic colon cancer. We assessed this model together with AOM and DSS-only models for their applicability to early detection of cancer. All AOM/DSS-treated mice produced autoantibodies to tumor-associated antigens analogous to those observed in human colon cancer patients. Autoantibody response was related to tumor antigen overexpression. Cancer autoantibodies were detected 21 days after starting treatment, when no malignant histopathological features were detectable, and they increased according to tumor progression. When carcinogenesis was induced separately by AOM or DSS, only those mice that developed malignant lesions produced significant levels of autoantibodies. These findings demonstrate that autoantibody development is an early event in tumorigenesis and validates its use for preclinical colon cancer diagnosis.

  5. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

    NASA Astrophysics Data System (ADS)

    Li, Chuan; Sanchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego; Vásquez, Rafael E.

    2016-08-01

    Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and vibratory measurements in such mechanical devices are all sensitive to the existence of faults. This work addresses the use of a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously. The statistical parameters of the wavelet packet transform (WPT) are first produced from the AE signal and the vibratory signal, respectively. Two deep Boltzmann machines (DBMs) are then developed for deep representations of the WPT statistical parameters. A random forest is finally suggested to fuse the outputs of the two DBMs as the integrated DRFF model. The proposed DRFF technique is evaluated using gearbox fault diagnosis experiments under different operational conditions, and achieves 97.68% of the classification rate for 11 different condition patterns. Compared to other peer algorithms, the addressed method exhibits the best performance. The results indicate that the deep learning fusion of acoustic and vibratory signals may improve fault diagnosis capabilities for gearboxes.

  6. A preliminary study of breast cancer diagnosis using laboratory based small angle x-ray scattering

    NASA Astrophysics Data System (ADS)

    Round, A. R.; Wilkinson, S. J.; Hall, C. J.; Rogers, K. D.; Glatter, O.; Wess, T.; Ellis, I. O.

    2005-09-01

    Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.

  7. Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm

    NASA Astrophysics Data System (ADS)

    Yuan, Shengfa; Chu, Fulei

    2007-04-01

    Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimisation principle that exhibits good generalisation when fault samples are few, it is especially fit for classification, forecasting and estimation in small-sample cases such as fault diagnosis, but some parameters in SVM are selected by man's experience, this has hampered its efficiency in practical application. Artificial immunisation algorithm (AIA) is used to optimise the parameters in SVM in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the SVM classifier is improved. The fault diagnosis of turbo pump rotor shows that the SVM optimised by AIA can give higher recognition accuracy than the normal SVM.

  8. Arrhythmias in athletes: evidence-based strategies and challenges for diagnosis, management, and sports eligibility.

    PubMed

    Fragakis, Nikolaos; Pagourelias, Efstathios D; Koskinas, Konstantinos C; Vassilikos, Vassilios

    2013-01-01

    Assessment and management of cardiac rhythm disorders in athletes is particularly challenging. An accurate diagnosis and optimal risk-stratification are often limited because of substantial phenotypic overlap between pathological entities and adaptive cardiovascular responses that normally occur in athletes. An accurate diagnosis, however, is particularly important in this population, as 2 competing risks need to be cautiously balanced: the risk of under-diagnosis of an arrhythmogenic substrate that may trigger life-threatening events versus the risk of over-diagnosis that may result in an athlete's improper disqualification. Accordingly, the management of arrhythmias in athletes may pose therapeutic dilemmas, and often differs substantially compared with the general population. In this review, we present the most frequently observed arrhythmias in athletes and briefly discuss their pathophysiologic substrate. We further propose diagnostic and therapeutic strategies based upon current guidelines, official recommendations, and emerging evidence from relevant clinical investigations. We focus particularly on disparities in current guidelines regarding the management of certain rhythm disorders, as these areas of uncertainty may reflect the challenging nature of these disorders and may indicate the need for individualized approaches in every-day clinical practice. A better understanding of the normal electrophysiological responses to chronic exercise, and of the pathophysiological basis and the true clinical significance of arrhythmias in athletes, may enhance decision-making, and may allow for management strategies which more prudently weigh the risk-to-benefit ratio of each approach.

  9. Diagnosis of IBS: symptoms, symptom-based criteria, biomarkers or 'psychomarkers'?

    PubMed

    Sood, Ruchit; Law, Graham R; Ford, Alexander C

    2014-11-01

    IBS is estimated to have a prevalence of up to 20% in Western populations and results in substantial costs to health-care services worldwide, estimated to be US$1 billion per year in the USA. IBS remains difficult to diagnose due to its multifactorial aetiology, heterogeneous nature and overlap of symptoms with organic pathologies, such as coeliac disease and IBD. As a result, IBS often continues to be a diagnosis of exclusion, resulting in unnecessary investigations. Available methods for the diagnosis of IBS-including the current gold standard, the Rome III criteria-perform only moderately well. Visceral hypersensitivity and altered pain perception do not discriminate between IBS and other functional gastrointestinal diseases or health with any great accuracy. Attention has now turned to developing novel biomarkers and using psychological markers (so-called psychomarkers) to aid the diagnosis of IBS. This Review describes how useful symptoms, symptom-based criteria, biomarkers and psychomarkers, and indeed combinations of all these approaches, are in the diagnosis of IBS. Future directions in diagnosing IBS could include combining demographic data, gastrointestinal symptoms, biomarkers and psychomarkers using statistical methods. Latent class analysis to distinguish between IBS and non-IBS symptom profiles might also represent a promising avenue for future research.

  10. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

    PubMed Central

    Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

    2016-01-01

    Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

  11. Differential diagnosis of ovarian tumors based primarily on their patterns and cell types.

    PubMed

    Young, R H; Scully, R E

    2001-08-01

    The differential diagnosis of ovarian tumors is reviewed based on their patterns and cell types. This approach, which differs from the standard textbook discussion of each neoplasm as an entity, has practical value as differential diagnosis depends largely on the pattern or patterns and cell type or types of tumors. Awareness of the broad range of lesions that may exhibit particular patterns or contain one or more cell types is crucial in formulating a differential diagnosis. The following patterns are considered: moderate-to-large-glandular and hollow-tubular; solid tubular and pseudotubular; cords and ribbons; insular; trabecular; slit-like and reticular spaces; microglandular and microfollicular; macrofollicular and pseudomacrofollicular; papillary; diffuse; fibromatous-thecomatous; and biphasic and pseudobiphasic. The following cell types are considered: small round cells; spindle cells; mucinous cells, comprising columnar, goblet cell and signet ring cell subtypes; clear cells; hobnail cells; oxyphil cells; and transitional cells. The morphologic diversity of ovarian tumors poses many challenges; knowledge of the occurrence and frequency of these patterns and cell types in various tumors and tumor-like lesions is of paramount diagnostic importance. A specific diagnosis can usually be made by evaluating routinely stained slides, but much less often, special staining, immunohistochemical staining or, very rarely, ultrastructural examination is also required. Finally, clinical data, operative findings, and gross features of the lesions may provide important, and at times decisive diagnostic clues.

  12. Condition-based diagnosis of mechatronic systems using a fractional calculus approach

    NASA Astrophysics Data System (ADS)

    Gutiérrez-Carvajal, Ricardo Enrique; Flávio de Melo, Leonimer; Maurício Rosário, João; Tenreiro Machado, J. A.

    2016-07-01

    While fractional calculus (FC) is as old as integer calculus, its application has been mainly restricted to mathematics. However, many real systems are better described using FC equations than with integer models. FC is a suitable tool for describing systems characterised by their fractal nature, long-term memory and chaotic behaviour. It is a promising methodology for failure analysis and modelling, since the behaviour of a failing system depends on factors that increase the model's complexity. This paper explores the proficiency of FC in modelling complex behaviour by tuning only a few parameters. This work proposes a novel two-step strategy for diagnosis, first modelling common failure conditions and, second, by comparing these models with real machine signals and using the difference to feed a computational classifier. Our proposal is validated using an electrical motor coupled with a mechanical gear reducer.

  13. Practical Application of Model-based Programming and State-based Architecture to Space Missions

    NASA Technical Reports Server (NTRS)

    Horvath, Gregory; Ingham, Michel; Chung, Seung; Martin, Oliver; Williams, Brian

    2006-01-01

    A viewgraph presentation to develop models from systems engineers that accomplish mission objectives and manage the health of the system is shown. The topics include: 1) Overview; 2) Motivation; 3) Objective/Vision; 4) Approach; 5) Background: The Mission Data System; 6) Background: State-based Control Architecture System; 7) Background: State Analysis; 8) Overview of State Analysis; 9) Background: MDS Software Frameworks; 10) Background: Model-based Programming; 10) Background: Titan Model-based Executive; 11) Model-based Execution Architecture; 12) Compatibility Analysis of MDS and Titan Architectures; 13) Integrating Model-based Programming and Execution into the Architecture; 14) State Analysis and Modeling; 15) IMU Subsystem State Effects Diagram; 16) Titan Subsystem Model: IMU Health; 17) Integrating Model-based Programming and Execution into the Software IMU; 18) Testing Program; 19) Computationally Tractable State Estimation & Fault Diagnosis; 20) Diagnostic Algorithm Performance; 21) Integration and Test Issues; 22) Demonstrated Benefits; and 23) Next Steps

  14. Rule-based simulation models

    NASA Technical Reports Server (NTRS)

    Nieten, Joseph L.; Seraphine, Kathleen M.

    1991-01-01

    Procedural modeling systems, rule based modeling systems, and a method for converting a procedural model to a rule based model are described. Simulation models are used to represent real time engineering systems. A real time system can be represented by a set of equations or functions connected so that they perform in the same manner as the actual system. Most modeling system languages are based on FORTRAN or some other procedural language. Therefore, they must be enhanced with a reaction capability. Rule based systems are reactive by definition. Once the engineering system has been decomposed into a set of calculations using only basic algebraic unary operations, a knowledge network of calculations and functions can be constructed. The knowledge network required by a rule based system can be generated by a knowledge acquisition tool or a source level compiler. The compiler would take an existing model source file, a syntax template, and a symbol table and generate the knowledge network. Thus, existing procedural models can be translated and executed by a rule based system. Neural models can be provide the high capacity data manipulation required by the most complex real time models.

  15. Delayed diagnosis of central skull-base osteomyelitis with abscess: case report and learning points.

    PubMed

    Chawdhary, G; Hussain, S; Corbridge, R

    2017-01-01

    Central skull-base osteomyelitis (CSBO) is a rare life-threatening infection, usually resulting from medial spread of necrotising otitis externa. Here, we describe a case with no identifiable source of infection, causing a delay in diagnosis. An 80-year-old man with Crohn's disease treated with mesalazine presented with collapse and tonic-clonic seizure. Computed tomography and magnetic resonance imaging showed a nasopharyngeal mass that was initially thought to be a neoplasm. Awaiting formal biopsy, he represented with collapse and repeat imaging showed features of abscess formation. Review of previous scans revealed skull-base erosion and the diagnosis was revised to skull-base osteomyelitis. This is the first reported case of CSBO associated with mesalazine use, an aminosalicylate used in Crohn's disease. It is only the second reported case with abscess formation. We discuss the learning points in making a timely diagnosis and examine the potential association of factors such as mesalazine use and abscess formation in this case.

  16. Reclassification of clinical sleep disorders using traditional models of syndromic, neuroanatomic, pathophysiological and etiological diagnosis.

    PubMed

    Spitzer, A Robert

    2014-09-01

    more than one location, and the best predictor might be symptoms. These are issues that need to undergo careful study on a syndromic, anatomic and physiological bases. This novel model opens up new avenues for understanding central nervous system sleep disorders, providing testable hypotheses regarding diagnosis and treatment.

  17. Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Wang, T.; Gu, F.; Ball, A. D.

    2013-01-01

    Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time

  18. Metabolic profile at first-time schizophrenia diagnosis: a population-based cross-sectional study

    PubMed Central

    Horsdal, Henriette Thisted; Benros, Michael Eriksen; Köhler-Forsberg, Ole; Krogh, Jesper; Gasse, Christiane

    2017-01-01

    Objective Schizophrenia and/or antipsychotic drug use are associated with metabolic abnormalities; however, knowledge regarding metabolic status and physician’s monitoring of metabolic status at first schizophrenia diagnosis is sparse. We assessed the prevalence of monitoring for metabolic blood abnormalities and characterized the metabolic profiles in people with a first-time schizophrenia diagnosis. Methods This is a population-based cross-sectional study including all adults born in Denmark after January 1, 1955, with their first schizophrenia diagnosis between 2000 and 2012 in the Central Denmark Region. Information on metabolic parameters was obtained from a clinical laboratory information system. Associations were calculated using Wilcoxon rank-sum tests, chi-square tests, logistic regression, and Spearman’s correlation coefficients. Results A total of 2,452 people with a first-time schizophrenia diagnosis were identified, of whom 1,040 (42.4%) were monitored for metabolic abnormalities. Among those monitored, 58.4% had an abnormal lipid profile and 13.8% had an abnormal glucose profile. People who had previously filled prescription(s) for antipsychotic drugs were more likely to present an abnormal lipid measure (65.7% vs 46.8%, P<0.001) and abnormal glucose profile (16.4% vs 10.1%, P=0.01). Conclusion Metabolic abnormalities are common at first schizophrenia diagnosis, particularly among those with previous antipsychotic prescription(s). Increased metabolic abnormalities already present in the early phase of schizophrenia emphasize the need for increased monitoring and management. PMID:28280344

  19. Phenomenological models of vibration signals for condition monitoring and fault diagnosis of epicyclic gearboxes

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Liu, Zongyao; Lin, Jing; Lu, Fanbo

    2016-05-01

    Condition monitoring and fault diagnosis of epicyclic gearboxes using vibration signals are not as straightforward as that of fixed-axis gearboxes since epicyclic gearboxes behave quite differently from fixed-axis gearboxes in many aspects, like spectral structures. Aiming to present the spectral structures of vibration signals of epicyclic gearboxes, phenomenological models of vibration signals of epicyclic gearboxes are developed by algebraic equations and spectral structures of these models are deduced using Fourier series analysis. In the phenomenological models, all the possible vibration transfer paths from gear meshing points to a fixed transducer and the effects of angular shifts of planet gears on the spectral structures are considered. Accordingly, time-varying vibration transfer paths from sun-planet/ring-planet gear meshing points to the fixed transducer due to carrier rotation are given by window functions with different amplitudes. And an angular shift in one planet gear position is introduced in the process of modeling. After the theoretical derivations, three experiments are conducted on an epicyclic gearbox test rig and the spectral structures of collected vibration signals are analyzed. As a result, the effects of angular shifts of planet gears are verified, and the phenomenological models of vibration signals when a local fault occurs on the sun gear and the planet gear are validated, respectively. The experiment results demonstrate that the established phenomenological models in this paper are helpful to the condition monitoring and fault diagnosis of epicyclic gearboxes.

  20. A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery.

    PubMed

    Xue, Xiaoming; Zhou, Jianzhong

    2017-01-01

    To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application.

  1. Identifying Model-Based Reconfiguration Goals through Functional Deficiencies

    NASA Technical Reports Server (NTRS)

    Benazera, Emmanuel; Trave-Massuyes, Louise

    2004-01-01

    Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.

  2. Combining Particle Filters and Consistency-Based Approaches for Monitoring and Diagnosis of Stochastic Hybrid Systems

    NASA Technical Reports Server (NTRS)

    Narasimhan, Sriram; Dearden, Richard; Benazera, Emmanuel

    2004-01-01

    Fault detection and isolation are critical tasks to ensure correct operation of systems. When we consider stochastic hybrid systems, diagnosis algorithms need to track both the discrete mode and the continuous state of the system in the presence of noise. Deterministic techniques like Livingstone cannot deal with the stochasticity in the system and models. Conversely Bayesian belief update techniques such as particle filters may require many computational resources to get a good approximation of the true belief state. In this paper we propose a fault detection and isolation architecture for stochastic hybrid systems that combines look-ahead Rao-Blackwellized Particle Filters (RBPF) with the Livingstone 3 (L3) diagnosis engine. In this approach RBPF is used to track the nominal behavior, a novel n-step prediction scheme is used for fault detection and L3 is used to generate a set of candidates that are consistent with the discrepant observations which then continue to be tracked by the RBPF scheme.

  3. Optimal sinusoidal modelling of gear mesh vibration signals for gear diagnosis and prognosis

    NASA Astrophysics Data System (ADS)

    Man, Zhihong; Wang, Wenyi; Khoo, Suiyang; Yin, Juliang

    2012-11-01

    In this paper, the synchronous signal average of gear mesh vibration signals is modelled with the multiple modulated sinusoidal representations. The signal model parameters are optimised against the measured signal averages by using the batch learning of the least squares technique. With the optimal signal model, all components of a gear mesh vibration signal, including the amplitude modulations, the phase modulations and the impulse vibration component induced by gear tooth cracking, are identified and analysed with insight of the gear tooth crack development and propagation. In particular, the energy distribution of the impulse vibration signal, extracted from the optimal signal model, provides sufficient information for monitoring and diagnosing the evolution of the tooth cracking process, leading to the prognosis of gear tooth cracking. The new methodologies for gear mesh signal modelling and the diagnosis of the gear tooth fault development and propagation are validated with a set of rig test data, which has shown excellent performance.

  4. A Novel Local Learning based Approach With Application to Breast Cancer Diagnosis

    SciTech Connect

    Xu, Songhua; Tourassi, Georgia

    2012-01-01

    The purpose of this study is to develop and evaluate a novel local learning-based approach for computer-assisted diagnosis of breast cancer. Our new local learning based algorithm using the linear logistic regression method as its base learner is described. Overall, our algorithm will perform its stochastic searching process until the total allowed computing time is used up by our random walk process in identifying the most suitable population subdivision scheme and their corresponding individual base learners. The proposed local learning-based approach was applied for the prediction of breast cancer given 11 mammographic and clinical findings reported by physicians using the BI-RADS lexicon. Our database consisted of 850 patients with biopsy confirmed diagnosis (290 malignant and 560 benign). We also compared the performance of our method with a collection of publicly available state-of-the-art machine learning methods. Predictive performance for all classifiers was evaluated using 10-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Figure 1 reports the performance of 54 machine learning methods implemented in the machine learning toolkit Weka (version 3.0). We introduced a novel local learning-based classifier and compared it with an extensive list of other classifiers for the problem of breast cancer diagnosis. Our experiments show that the algorithm superior prediction performance outperforming a wide range of other well established machine learning techniques. Our conclusion complements the existing understanding in the machine learning field that local learning may capture complicated, non-linear relationships exhibited by real-world datasets.

  5. Model-Based Fault Diagnosis for Turboshaft Engines

    NASA Technical Reports Server (NTRS)

    Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.

    1998-01-01

    Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

  6. A Model-Based Diagnosis Framework for Distributed Systems

    DTIC Science & Technology

    2002-05-04

    none norn. none PPtI-video-failA P1 2 -video-foil Xvideo 6 audio video none none none. none Prt-audio-failA P 1 2 -video-/lil ADBI1fajil Table 1...audio, and Xvideo denotes degrated video. R computes a tree-decomposition in which each node of the tree is a clique, and undirected edges correspond to

  7. Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

    PubMed Central

    Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

    2017-01-01

    Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

  8. A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

    PubMed Central

    Ye, Qing; Pan, Hao; Liu, Changhua

    2015-01-01

    This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. PMID:25722717

  9. A framework for final drive simultaneous failure diagnosis based on fuzzy entropy and sparse bayesian extreme learning machine.

    PubMed

    Ye, Qing; Pan, Hao; Liu, Changhua

    2015-01-01

    This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F 1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.

  10. Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

    NASA Astrophysics Data System (ADS)

    Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

    2017-01-01

    Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

  11. Computer-aided diagnosis of prostate cancer with emphasis on ultrasound-based approaches: a review.

    PubMed

    Moradi, Mehdi; Mousavi, Parvin; Abolmaesumi, Purang

    2007-07-01

    This paper reviews the state of the art in computer-aided diagnosis of prostate cancer and focuses, in particular, on ultrasound-based techniques for detection of cancer in prostate tissue. The current standard procedure for diagnosis of prostate cancer, i.e., ultrasound-guided biopsy followed by histopathological analysis of tissue samples, is invasive and produces a high rate of false negatives resulting in the need for repeated trials. It is against these backdrops that the search for new methods to diagnose prostate cancer continues. Image-based approaches (such as MRI, ultrasound and elastography) represent a major research trend for diagnosis of prostate cancer. Due to the integration of ultrasound imaging in the current clinical procedure for detection of prostate cancer, we specifically provide a more detailed review of methodologies that use ultrasound RF-spectrum parameters, B-scan texture features and Doppler measures for prostate tissue characterization. We present current and future directions of research aimed at computer-aided detection of prostate cancer and conclude that ultrasound is likely to play an important role in the field.

  12. Evidence-based diagnosis of type 1 von Willebrand disease: a Bayes theorem approach.

    PubMed

    Tosetto, Alberto; Castaman, Giancarlo; Rodeghiero, Francesco

    2008-04-15

    The diagnosis of type 1 von Willebrand disease (VWD) is based on the presence of bleeding symptoms, reduced von Willebrand factor (VWF) levels, and autosomal inheritance of the phenotype. To better appreciate the contribution of clinical and laboratory data to the final diagnosis of VWD, we computed the likelihoods of having VWD as a function of the bleeding score (LR(score)), of VWF level (LR(VWF)), and of number of first-degree family members with reduced VWF levels (LR(family)). The 3 likelihoods were therefore combined using the Bayes theorem, giving the final probability (odds) of having VWD. LR(family) and LR(VWF) were the 2 factors mostly influencing the final probability of having VWD. Data from the present study provide an evidence-based description of the minimal criteria for the diagnosis of type 1 VWD. As an example, presence of VWF levels lower than 40 IU/dL in at least 2 family members (including the proband) and a bleeding score of at least 1 were found to be required for a final odd of VWD higher than 2.0 (false-positive rate less than one-half). Validation of this approach and of its clinical utility is, however, required by analysis in other cohorts of well-characterized type 1 VWD patients.

  13. Image-based computer-assisted diagnosis system for benign paroxysmal positional vertigo

    NASA Astrophysics Data System (ADS)

    Kohigashi, Satoru; Nakamae, Koji; Fujioka, Hiromu

    2005-04-01

    We develop the image based computer assisted diagnosis system for benign paroxysmal positional vertigo (BPPV) that consists of the balance control system simulator, the 3D eye movement simulator, and the extraction method of nystagmus response directly from an eye movement image sequence. In the system, the causes and conditions of BPPV are estimated by searching the database for record matching with the nystagmus response for the observed eye image sequence of the patient with BPPV. The database includes the nystagmus responses for simulated eye movement sequences. The eye movement velocity is obtained by using the balance control system simulator that allows us to simulate BPPV under various conditions such as canalithiasis, cupulolithiasis, number of otoconia, otoconium size, and so on. Then the eye movement image sequence is displayed on the CRT by the 3D eye movement simulator. The nystagmus responses are extracted from the image sequence by the proposed method and are stored in the database. In order to enhance the diagnosis accuracy, the nystagmus response for a newly simulated sequence is matched with that for the observed sequence. From the matched simulation conditions, the causes and conditions of BPPV are estimated. We apply our image based computer assisted diagnosis system to two real eye movement image sequences for patients with BPPV to show its validity.

  14. Hand-held probe based optical imaging system towards breast cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Ge, Jiajia; Jayachandran, Bhavani; Regalado, Steven; Zhu, Banghe; Godavarty, Anuradha

    2007-02-01

    Near-infrared (NIR) optical imaging is an emerging noninvasive modality for breast cancer diagnosis. However, the currently available optical imaging systems towards tomography studies are limited either by instrument portability, patient comfort, or flexibility to image any given tissue volume. Herein, a hand-held based optical imaging system is developed such that it can possibly overcome some of the above limitations. The unique features of the hand-held optical probe are: (i) to perform simultaneous multiple point illumination and detection, thus decreasing the total imaging time and improving the overall signal strength; (ii) to adapt to the contour of tissue surface, thus decreasing the leakage of excitation and emission signal at contact surface; and (iii) to obtain trans-illumination measurements apart from reflectance measurements, thus improving the depth information. The increased detected signal strength as well as total interrogated tissue volume is demonstrated by simulation studies (i.e. forward model) over a 5×10×10 cc slab phantom. The appropriate number and layout of the source and detection points on the probe head is determined and the hand-held optical probe is developed. A frequency-domain ICCD (intensified charge coupled device) detection system, which allows simultaneous multiple points detection, is developed and coupled to the hand-held probe in order to perform fluorescence-enhanced optical imaging of tissue phantoms. In the future, imaging of homogenous liquid phantoms will be used for the assessment of this hand-held system, followed by extensive imaging studies on different phantoms types under various experimental conditions.

  15. Remote Fault Information Acquisition and Diagnosis System of the Combine Harvester Based on LabVIEW

    NASA Astrophysics Data System (ADS)

    Chen, Jin; Wu, Pei; Xu, Kai

    Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.

  16. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis.

    PubMed

    Li, Qiang; Chen, Huiling; Huang, Hui; Zhao, Xuehua; Cai, ZhenNao; Tong, Changfei; Liu, Wenbin; Tian, Xin

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.

  17. Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis

    NASA Astrophysics Data System (ADS)

    Gu, Xiaohui; Yang, Shaopu; Liu, Yongqiang; Hao, Rujiang

    2016-12-01

    Envelope analysis is one of the most useful methods in localized fault diagnosis of rolling element bearings. However, there is a challenge in selecting the optimal resonance band. In this paper, a novel method based on kurtogram and frequency domain correlated kurtosis is proposed. To obtain the correct relationship between the node and frequency band in wavelet packet transform, a vital process named frequency ordering is conducted to solve the frequency folding problem due to down sampling. Correlated kurtosis of envelope spectrum instead of correlated kurtosis of envelope signal or kurtosis of envelope spectrum is utilized to generate the kurtogram, in which the maximum value can indicate the optimal band for envelope analysis. Several cases of experimental bearing fault signals are used to evaluate the immunity of the proposed method to strong noise interference. The improved performance has also been compared with two previous developed methods. The results demonstrate the effectiveness and robustness of the method in fault diagnosis of rolling element bearings.

  18. Quality of life in Chinese home-based advanced cancer patients: does awareness of cancer diagnosis matter?

    PubMed

    Fan, Xiaoping; Huang, Hua; Luo, Qiong; Zhou, Jiying; Tan, Ge; Yong, Na

    2011-10-01

    The aim of this study was to assess the quality of life (QOL) of Chinese home-based advanced-stage cancer patients and to evaluate the association between the disclosure of cancer diagnosis and QOL. An interview-based survey was conducted from December 2009 to June 2010 in the home-based hospice of the First Affiliated Hospital of Chongqing Medical University, China. The principal finding of this study demonstrated that patients who did not have knowledge of their diagnosis exhibited better physical and emotional QOL compared with those who had knowledge of their diagnosis.

  19. Model based vibration monitoring

    SciTech Connect

    Esat, I.; Paya, B.; Badi, M.N.M.

    1996-11-01

    The principal source of vibratory excitation of gear system is the unsteady component of the relative angular motion of pair of meshing spur gears. This vibratory excitation is described by the transmission error. The transmission error present itself as a varying force at the contact point of the meshing gear teeth. The varying force is also influenced by the varying tooth stiffness due to change of orientation of teeth relative to each other, during the contact phase of each pair. Such a varying force produces both lateral and torsional excitation to the gear system. This paper presents analytical formulation of a simple two meshing spur gear system as a three mass system (18 DOF). The mathematical model also incorporates the analytical formulation of the tooth stiffness. The analytical results are compared with the experimental results. At this stage of analysis the procedure developed for handling the nonlinear influences of the tooth geometry is not fully implemented and the tooth stiffness taken as a constant value representing the average tooth stiffness. The comparison between the analytical and experimental results are encouraging as three main frequency obtained from FFT of the experimental results correlates very closely with the analytical results.

  20. APPLYING TENSOR-BASED MORPHOMETRY TO PARAMETRIC SURFACES CAN IMPROVE MRI-BASED DISEASE DIAGNOSIS

    PubMed Central

    Wang, Yalin; Yuan, Lei; Shi, Jie; Greve, Alexander; Ye, Jieping; Toga, Arthur W.; Reiss, Allan L.; Thompson, Paul M.

    2013-01-01

    Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an ℓ1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification. PMID:23435208

  1. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    PubMed

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2016-10-20

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  2. Model-Based Method for Sensor Validation

    NASA Technical Reports Server (NTRS)

    Vatan, Farrokh

    2012-01-01

    Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

  3. Modeling Guru: Knowledge Base for NASA Modelers

    NASA Astrophysics Data System (ADS)

    Seablom, M. S.; Wojcik, G. S.; van Aartsen, B. H.

    2009-05-01

    Modeling Guru is an on-line knowledge-sharing resource for anyone involved with or interested in NASA's scientific models or High End Computing (HEC) systems. Developed and maintained by the NASA's Software Integration and Visualization Office (SIVO) and the NASA Center for Computational Sciences (NCCS), Modeling Guru's combined forums and knowledge base for research and collaboration is becoming a repository for the accumulated expertise of NASA's scientific modeling and HEC communities. All NASA modelers and associates are encouraged to participate and provide knowledge about the models and systems so that other users may benefit from their experience. Modeling Guru is divided into a hierarchy of communities, each with its own set forums and knowledge base documents. Current modeling communities include those for space science, land and atmospheric dynamics, atmospheric chemistry, and oceanography. In addition, there are communities focused on NCCS systems, HEC tools and libraries, and programming and scripting languages. Anyone may view most of the content on Modeling Guru (available at http://modelingguru.nasa.gov/), but you must log in to post messages and subscribe to community postings. The site offers a full range of "Web 2.0" features, including discussion forums, "wiki" document generation, document uploading, RSS feeds, search tools, blogs, email notification, and "breadcrumb" links. A discussion (a.k.a. forum "thread") is used to post comments, solicit feedback, or ask questions. If marked as a question, SIVO will monitor the thread, and normally respond within a day. Discussions can include embedded images, tables, and formatting through the use of the Rich Text Editor. Also, the user can add "Tags" to their thread to facilitate later searches. The "knowledge base" is comprised of documents that are used to capture and share expertise with others. The default "wiki" document lets users edit within the browser so others can easily collaborate on the

  4. Model-Based Safety Analysis

    NASA Technical Reports Server (NTRS)

    Joshi, Anjali; Heimdahl, Mats P. E.; Miller, Steven P.; Whalen, Mike W.

    2006-01-01

    System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical.

  5. Retinoblastoma diagnosis: a proposal based on the experience of Centro Infantil Boldrini, Brazil.

    PubMed

    Palazzi, Maristela A; Stephan, Celso; Brandalise, Silvia R; Aguiar, Simone Dos Santos

    2013-08-01

    Advanced disease is a risk factor for eye loss in patients with retinoblastoma (RB). We still record critical rates of enucleation, especially for unilateral RB due to advanced stages of disease at diagnosis. This retrospective study of 223 RB patient records referred to treatment at Centro Infantil Boldrini, Brazil, between 1978 and 2008, showed that 176 patients (79%) presented intraocular tumors while 47 (21%) already had extraocular involvement. At the time of diagnosis, the age of patients was 26.2 months in the group that had enucleated eyes and 13.7 months in the group that preserved both eyes. Under a multiple logistic regression model, familial history (OR = 0.195; p = .01) and age at diagnosis in months (OR = 1.047; p = .04) were significantly correlated with enucleation. Strategies to early detect RB must be changed in order to offer better chances of ocular preservation with visual function. Authors propose a systematic referral of all children to the ophthalmologist for an indirect ophthalmoscopy once a year in the first two years of life, as a measure to be adopted by all pediatricians in daily routine to early detect the tumor.

  6. Next generation sequencing for molecular diagnosis of neurological disorders using ataxias as a model.

    PubMed

    Németh, Andrea H; Kwasniewska, Alexandra C; Lise, Stefano; Parolin Schnekenberg, Ricardo; Becker, Esther B E; Bera, Katarzyna D; Shanks, Morag E; Gregory, Lorna; Buck, David; Zameel Cader, M; Talbot, Kevin; de Silva, Rajith; Fletcher, Nicholas; Hastings, Rob; Jayawant, Sandeep; Morrison, Patrick J; Worth, Paul; Taylor, Malcolm; Tolmie, John; O'Regan, Mary; Valentine, Ruth; Packham, Emily; Evans, Julie; Seller, Anneke; Ragoussis, Jiannis

    2013-10-01

    Many neurological conditions are caused by immensely heterogeneous gene mutations. The diagnostic process is often long and complex with most patients undergoing multiple invasive and costly investigations without ever reaching a conclusive molecular diagnosis. The advent of massively parallel, next-generation sequencing promises to revolutionize genetic testing and shorten the 'diagnostic odyssey' for many of these patients. We performed a pilot study using heterogeneous ataxias as a model neurogenetic disorder to assess the introduction of next-generation sequencing into clinical practice. We captured 58 known human ataxia genes followed by Illumina Next-Generation Sequencing in 50 highly heterogeneous patients with ataxia who had been extensively investigated and were refractory to diagnosis. All cases had been tested for spinocerebellar ataxia 1-3, 6, 7 and Friedrich's ataxia and had multiple other biochemical, genetic and invasive tests. In those cases where we identified the genetic mutation, we determined the time to diagnosis. Pathogenicity was assessed using a bioinformatics pipeline and novel variants were validated using functional experiments. The overall detection rate in our heterogeneous cohort was 18% and varied from 8.3% in those with an adult onset progressive disorder to 40% in those with a childhood or adolescent onset progressive disorder. The highest detection rate was in those with an adolescent onset and a family history (75%). The majority of cases with detectable mutations had a childhood onset but most are now adults, reflecting the long delay in diagnosis. The delays were primarily related to lack of easily available clinical testing, but other factors included the presence of atypical phenotypes and the use of indirect testing. In the cases where we made an eventual diagnosis, the delay was 3-35 years (mean 18.1 years). Alignment and coverage metrics indicated that the capture and sequencing was highly efficient and the consumable cost

  7. A dynamic integrated fault diagnosis method for power transformers.

    PubMed

    Gao, Wensheng; Bai, Cuifen; Liu, Tong

    2015-01-01

    In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.

  8. A Dynamic Integrated Fault Diagnosis Method for Power Transformers

    PubMed Central

    Gao, Wensheng; Liu, Tong

    2015-01-01

    In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified. PMID:25685841

  9. Diffuse lung disease of infancy: a pattern-based, algorithmic approach to histological diagnosis.

    PubMed

    Armes, Jane E; Mifsud, William; Ashworth, Michael

    2015-02-01

    Diffuse lung disease (DLD) of infancy has multiple aetiologies and the spectrum of disease is substantially different from that seen in older children and adults. In many cases, a specific diagnosis renders a dire prognosis for the infant, with profound management implications. Two recently published series of DLD of infancy, collated from the archives of specialist centres, indicate that the majority of their cases were referred, implying that the majority of biopsies taken for DLD of infancy are first received by less experienced pathologists. The current literature describing DLD of infancy takes a predominantly aetiological approach to classification. We present an algorithmic, histological, pattern-based approach to diagnosis of DLD of infancy, which, with the aid of appropriate multidisciplinary input, including clinical and radiological expertise and ancillary diagnostic studies, may lead to an accurate and useful interim report, with timely exclusion of inappropriate diagnoses. Subsequent referral to a specialist centre for confirmatory diagnosis will be dependent on the individual case and the decision of the multidisciplinary team.

  10. Diagnosis of Oropouche Virus Infection Using a Recombinant Nucleocapsid Protein-Based Enzyme Immunoassay

    PubMed Central

    Saeed, Mohammad F.; Nunes, Marcio; Vasconcelos, Pedro F.; Travassos Da Rosa, Amelia P. A.; Watts, Douglas M.; Russell, Kevin; Shope, Robert E.; Tesh, Robert B.; Barrett, Alan D. T.

    2001-01-01

    Oropouche (ORO) virus is an emerging infectious agent that has caused numerous outbreaks of an acute febrile (dengue-like) illness among humans in Brazil, Peru, and Panama. Diagnosis of ORO virus infection is based mainly on serology. Two different antigens, hamster serum antigen (HSA) and Vero cell lysate antigen (VCLA), are currently used in enzyme immunoassays (EIAs) in Brazil and Peru, respectively, to investigate the epidemiology of ORO virus infection. Both antigens involve use of infectious virus, and for this reason their use is restricted. Consequently, the frequency and distribution of ORO virus infection are largely unexplored in other countries of South America. This report describes the use of a bacterially expressed recombinant nucleocapsid (rN) protein of ORO virus in EIAs for the diagnosis of ORO virus infection. The data revealed that the purified rN protein is comparable to the authentic viral N protein in its antigenic characteristics and is highly sensitive and specific in EIAs. Among 183 serum samples tested, a high degree of concordance was found between rN protein-based EIA and HSA- and VCLA-based EIAs for the detection of both ORO virus-specific immunoglobulin M (IgM) and IgG antibodies. The high sensitivity, specificity, and safety of the rN protein-based EIA make it a useful diagnostic technique that can be widely used to detect ORO virus infection in South America. PMID:11427552

  11. Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the “i-ROP” System and Image Features Associated With Expert Diagnosis

    PubMed Central

    Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J. Peter; Bozkurt, Alican; Erdogmus, Deniz; Kalpathy-Cramer, Jayashree; Patel, Samir; Jonas, Karyn; Chan, R. V. Paul; Ostmo, Susan; Chiang, Michael F.

    2015-01-01

    Purpose We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. Methods A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the “i-ROP” system. Results Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). Conclusions This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Translational Relevance Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists. PMID:26644965

  12. Multiplexed bead-based mesofluidic system for gene diagnosis and genotyping.

    PubMed

    Jin, Sheng-Quan; Ye, Bang-Ce; Huo, Hao; Zeng, Ai-Jun; Xie, Cheng-Ke; Ren, Bing-Qiang; Huang, Hui-Jie

    2010-12-01

    We have developed a novel multiplexed bead-based mesofluidic system (MBMS) based on the specific recognition events on the surface of a series of microbeads (diameter 250 μm) arranged in polydimethylsiloxane (PDMS) microchannels (diameter 300 μm) with the predetermined order and assembled an apparatus implementing automatically the high-throughput bead-based assay and further demonstrated its feasibility and flexibility of gene diagnosis and genotyping, such as β-thalassemia mutation detection and HLA-DQA genotyping. The apparatus, consisting of bead-based mesofluidic PDMS chip, liquid-processing module, and fluorescence detection module, can integrate the procedure of automated-sampling, hybridization reactions, washing, and in situ fluorescence detection. The results revealed that MBMS is fast, has high sensitivity, and can be automated to carry out parallel and multiplexed genotyping and has the potential to open up new routes to flexible, high-throughput approaches for bioanalysis.

  13. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    NASA Astrophysics Data System (ADS)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-12-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  14. Bond graph modeling and experimental verification of a novel scheme for fault diagnosis of rolling element bearings in special operating conditions

    NASA Astrophysics Data System (ADS)

    Mishra, C.; Samantaray, A. K.; Chakraborty, G.

    2016-09-01

    Vibration analysis for diagnosis of faults in rolling element bearings is complicated when the rotor speed is variable or slow. In the former case, the time interval between the fault-induced impact responses in the vibration signal are non-uniform and the signal strength is variable. In the latter case, the fault-induced impact response strength is weak and generally gets buried in the noise, i.e. noise dominates the signal. This article proposes a diagnosis scheme based on a combination of a few signal processing techniques. The proposed scheme initially represents the vibration signal in terms of uniformly resampled angular position of the rotor shaft by using the interpolated instantaneous angular position measurements. Thereafter, intrinsic mode functions (IMFs) are generated through empirical mode decomposition (EMD) of resampled vibration signal which is followed by thresholding of IMFs and signal reconstruction to de-noise the signal and envelope order tracking to diagnose the faults. Data for validating the proposed diagnosis scheme are initially generated from a multi-body simulation model of rolling element bearing which is developed using bond graph approach. This bond graph model includes the ball and cage dynamics, localized fault geometry, contact mechanics, rotor unbalance, and friction and slip effects. The diagnosis scheme is finally validated with experiments performed with the help of a machine fault simulator (MFS) system. Some fault scenarios which could not be experimentally recreated are then generated through simulations and analyzed through the developed diagnosis scheme.

  15. Weight-based nutritional diagnosis of Mexican children and adolescents with neuromotor disabilities

    PubMed Central

    2012-01-01

    Background Nutrition related problems are increasing worldwide but they have scarcely been evaluated in people with neuromotor disabilities, particularly in developing countries. In this study our aim was to describe the weight-based nutritional diagnoses of children and adolescents with neuromotor disabilities who attended a private rehabilitation center in Mexico City. Methods Data from the first visit’s clinical records of 410 patients who attended the Nutrition department at the Teleton Center for Children Rehabilitation, between 1999 and 2008, were analyzed. Sex, age, weight and height, length or segmental length data were collected and used to obtain the nutritional diagnosis based on international growth charts, as well as disability-specific charts. Weight for height was considered the main indicator. Results Cerebral palsy was the most frequent diagnosis, followed by spina bifida, muscular dystrophy, and Down’s syndrome. Children with cerebral palsy showed a higher risk of presenting low weight/undernutrition (LW/UN) than children with other disabilities, which was three times higher in females. In contrast, children with spina bifida, particularly males, were more likely to be overweight/obese (OW/OB), especially after the age of 6 and even more after 11. Patients with muscular dystrophy showed a significantly lower risk of LW/UN than patients with other disabilities. In patients with Down’s syndrome neither LW/UN nor OW/OB were different between age and sex. Conclusions This is the first study that provides evidence of the nutritional situation of children and adolescents with neuromotor disabilities in Mexico, based on their weight status. Low weight and obesity affect a large number of these patients due to their disability, age and sex. Early nutritional diagnosis must be considered an essential component in the treatment of these patients to prevent obesity and malnutrition, and improve their quality of life. PMID:22559790

  16. Toward dynamic model-based prognostics for transmission gears

    NASA Astrophysics Data System (ADS)

    Wang, Wenyi

    2002-07-01

    This paper presents a novel methodology for the diagnosis and prognosis of crucial gear faults, such as gear tooth fatigue cracking. Currently, an effective detection of tooth cracking can be achieved by using the autoregressive (AR) modeling approach, where the gear vibration signal is modeled by an AR model and gear tooth cracking is detected by identifying the sudden changes in the model's error signal. The model parameters can be estimated under the criteria of minimum power or maximum kurtosis of model errors. However, these model parameters possess no physical meaning about the monitored gear system. It is proposed that the AR model be replaced by a gear dynamics model (GDM) that contains physically meaningful parameters, such as mass, damping and stiffness. By identifying and tracking the changes in the parameters, it is possible to make diagnosis and prognosis of gear faults. For example, a reduction in mesh stiffness may indicate cracking of a gear tooth. Towards physical model-based prognosis, an adaptive (or optimization) strategy has been developed for approximating a gear signal using a simplified gear signal model. Preliminary results show that this strategy provides a feasible adaptive process for updating model parameters based on measured gear signal.

  17. A web-based knowledge management system integrating Western and Traditional Chinese Medicine for relational medical diagnosis.

    PubMed

    Herrera-Hernandez, Maria C; Lai-Yuen, Susana K; Piegl, Les A; Zhang, Xiao

    2016-10-26

    This article presents the design of a web-based knowledge management system as a training and research tool for the exploration of key relationships between Western and Traditional Chinese Medicine, in order to facilitate relational medical diagnosis integrating these mainstream healing modalities. The main goal of this system is to facilitate decision-making processes, while developing skills and creating new medical knowledge. Traditional Chinese Medicine can be considered as an ancient relational knowledge-based approach, focusing on balancing interrelated human functions to reach a healthy state. Western Medicine focuses on specialties and body systems and has achieved advanced methods to evaluate the impact of a health disorder on the body functions. Identifying key relationships between Traditional Chinese and Western Medicine opens new approaches for health care practices and can increase the understanding of human medical conditions. Our knowledge management system was designed from initial datasets of symptoms, known diagnosis and treatments, collected from both medicines. The datasets were subjected to process-oriented analysis, hierarchical knowledge representation and relational database interconnection. Web technology was implemented to develop a user-friendly interface, for easy navigation, training and research. Our system was prototyped with a case study on chronic prostatitis. This trial presented the system's capability for users to learn the correlation approach, connecting knowledge in Western and Traditional Chinese Medicine by querying the database, mapping validated medical information, accessing complementary information from official sites, and creating new knowledge as part of the learning process. By addressing the challenging tasks of data acquisition and modeling, organization, storage and transfer, the proposed web-based knowledge management system is presented as a tool for users in medical training and research to explore, learn and

  18. [Complex method of the diagnosis of the mandibular injury based of informational technologies].

    PubMed

    Korotkikh, N G; Bakhmet'ev, V I; Shalaev, O Iu; Antimenko, O O

    2004-01-01

    Special method of complex diagnosis of mandibular injury is under consideration. It is based on the informational technologies. The study of the mechanisms of the injury's formation has been made on 109 patients of the skull-jaw-facial surgery department of the hospital were under investigation. Two main types of jaw-facial injuries have been revealed. The first type: falling down from the height of one's own size (stature). The second type: blow (stroke) of a blunt object. The decrease of the number of the inflammatory complications of the broken jaw due to the usage of new algorithms on the basis of informational technologies has been noted.

  19. Diagnosis of subharmonic faults of large rotating machinery based on EMD

    NASA Astrophysics Data System (ADS)

    Wu, Fangji; Qu, Liangsheng

    2009-02-01

    The vibration signals always carry the abundant dynamic information of a machine and are very useful for the feature extraction and fault diagnosis. In practice, most subharmonic signals have a close relationship to time variables and can manifest large amplitude fluctuation, transient vibration, or modulation signals in time domain. In view of this, this paper describes an effective method to search the features of subharmonic faults of large rotating machinery based on empirical mode decomposition (EMD). Case study on some actual vibration signals of machine parts shows that EMD is an adaptive and unsupervised method in feature extraction and it provides an attractive alternative to the traditional diagnostic methods.

  20. Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines.

    PubMed

    Islam, M M Manjurul; Kim, Jaeyoung; Khan, Sheraz A; Kim, Jong-Myon

    2017-02-01

    This letter presents a multi-fault diagnosis scheme for bearings using hybrid features extracted from their acoustic emissions and a Bayesian inference-based one-against-all support vector machine (Bayesian OAASVM) for multi-class classification. The Bayesian OAASVM, which is a standard multi-class extension of the binary support vector machine, results in ambiguously labeled regions in the input space that degrade its classification performance. The proposed Bayesian OAASVM formulates the feature space as an appropriate Gaussian process prior, interprets the decision value of the Bayesian OAASVM as a maximum a posteriori evidence function, and uses Bayesian inference to label unknown samples.

  1. A feature extraction method based on information theory for fault diagnosis of reciprocating machinery.

    PubMed

    Wang, Huaqing; Chen, Peng

    2009-01-01

    This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to.

  2. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery

    PubMed Central

    Wang, Huaqing; Chen, Peng

    2009-01-01

    This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to. PMID:22574021

  3. Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree.

    PubMed

    Karar, Mohamed Esmail; El-Khafif, Sahar H; El-Brawany, Mohamed A

    2017-04-01

    In order to assist the diagnosis procedure of heart sound signals, this paper presents a new automated method for classifying the heart status using a rule-based classification tree into normal and three abnormal cases; namely the aortic valve stenosis, aortic insufficient, and ventricular septum defect. The developed method includes three main steps as follows. First, one cycle of the heart sound signals is automatically detected and segmented based on time properties of the heart signals. Second, the segmented cycle is preprocessed with the discrete wavelet transform and then largest Lyapunov exponents are calculated to generate the dynamical features of heart sound time series. Finally, a rule-based classification tree is fed by these Lyapunov exponents to give the final decision of the heart health status. The developed method has been tested successfully on twenty-two datasets of normal heart sounds and murmurs with success rate of 95.5%. The resulting error can be easily corrected by modifying the classification rules; consequently, the accuracy of automated heart sounds diagnosis is further improved.

  4. Application of the rank-based method to DNA methylation for cancer diagnosis.

    PubMed

    Li, Hongdong; Hong, Guini; Xu, Hui; Guo, Zheng

    2015-01-25

    Detecting aberrant DNA methylation as diagnostic or prognostic biomarkers for cancer has been a topic of considerable interest recently. However, current classifiers based on absolute methylation values detected from a cohort of samples are typically difficult to be transferable to other cohorts of samples. Here, focusing on relative methylation levels, we employed a modified rank-based method to extract reversal pairs of CpG sites whose relative methylation level orderings differ between disease samples and normal controls for cancer diagnosis. The reversal pairs identified for five cancer types respectively show excellent prediction performance with the accuracy above 95%. Furthermore, when evaluating the reversal pairs identified for one cancer type in an independent cohorts of samples, we found that they could distinguish different subtypes of this cancer or different malignant stages including early stage of this cancer from normal controls. The identified reversal pairs also appear to be specific to cancer type. In conclusion, the reversal pairs detected by the rank-based method could be used for accurate cancer diagnosis, which are transferable to independent cohorts of samples.

  5. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals.

    PubMed

    Zhang, Wei; Peng, Gaoliang; Li, Chuanhao; Chen, Yuanhang; Zhang, Zhujun

    2017-02-22

    Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.

  6. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

    PubMed Central

    Zhang, Wei; Peng, Gaoliang; Li, Chuanhao; Chen, Yuanhang; Zhang, Zhujun

    2017-01-01

    Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions. PMID:28241451

  7. Model-based machine learning.

    PubMed

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

  8. Model-based machine learning

    PubMed Central

    Bishop, Christopher M.

    2013-01-01

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

  9. Blood culture-based diagnosis of bacteraemia: state of the art.

    PubMed

    Opota, O; Croxatto, A; Prod'hom, G; Greub, G

    2015-04-01

    Blood culture remains the best approach to identify the incriminating microorganisms when a bloodstream infection is suspected, and to guarantee that the antimicrobial treatment is adequate. Major improvements have been made in the last years to increase the sensitivity and specificity and to reduce the time to identification of microorganisms recovered from blood cultures. Among other factors, the introduction in clinical microbiology laboratories of the matrix-assisted laser desorption ionization time-of-flight mass spectrometry technology revolutionized the identification of microorganisms whereas the introduction of nucleic-acid-based methods, such as DNA hybridization or rapid PCR-based test, significantly reduce the time to results. Together with traditional antimicrobial susceptibility testing, new rapid methods for the detection of resistance mechanisms respond to major epidemiological concerns such as methicillin-resistant Staphylococcus aureus, extended-spectrum β-lactamase or carbapenemases. This review presents and discusses the recent developments in microbial diagnosis of bloodstream infections based on blood cultures.

  10. In vivo diagnosis of esophageal cancer using image-guided Raman endoscopy and biomolecular modeling.

    PubMed

    Bergholt, M S; Zheng, W; Lin, K; Ho, K Y; Teh, M; Yeoh, K G; So, J B; Huang, Z

    2011-04-01

    The aim of this work was to evaluate the biochemical foundation and clinical merit of multimodal image-guided Raman endoscopy technique for real-time in vivo diagnosis of cancer in the esophagus during clinical endoscopic examinations. A novel fiber-optic Raman endoscopy system was utilized for in vivo esophageal Raman measurements at 785 nm laser excitation within 0.5 second under the multimodal wide-field endoscopic imaging (white light reflectance (WLR) imaging, narrow-band imaging (NBI) and autofluorescence imaging (AFI) guidance. A total of 75 esophageal tissue sites from 27 patients were measured, in which 42 in vivo Raman spectra were from normal tissues and 33 in vivo Raman spectra were from malignant tumors as confirmed by histopathology. The biomolecular modeling (non-negativity-constrained least-squares minimization (NNCLSM) utilizing six basis reference spectra from the representative biochemicals (i.e., actin, collagen, DNA, histones, triolein and glycogen) were employed to estimate the biochemical compositions of esophageal tissue. The resulting diagnostically significant fit coefficients were further utilized through linear discriminant analysis (LDA) and leave-one tissue site-out, cross validation method to develop diagnostic algorithms for esophageal cancer diagnosis. High-quality in vivo Raman spectra in the range of 800-1800 cm-1 can be acquired from normal and cancerous esophageal mucosa in real-time under multimodal endoscopic imaging guidance. Esophageal cancer tissue showed distinct Raman signals mainly associated with cell proliferation, lipid reduction, abnormal nuclear activity and neovasculation. The fit coefficients for actin, DNA, histones, triolein, and glycogen were found to be most significant for construction of the LDA diagnostic model, giving rise to an accuracy of 96.0% (i.e., sensitivity of 97.0% and specificity of 95.2%) for in vivo diagnosis of esophageal cancer. This study demonstrates that multimodal image-guided Raman

  11. The Fusion Model for Skills Diagnosis: Blending Theory with Practicality. Research Report. ETS RR-08-71

    ERIC Educational Resources Information Center

    Hartz, Sarah; Roussos, Louis

    2008-01-01

    This paper presents the development of the fusion model skills diagnosis system (fusion model system), which can help integrate standardized testing into the learning process with both skills-level examinee parameters for modeling examinee skill mastery and skills-level item parameters, giving information about the diagnostic power of the test.…

  12. Home-based Diagnosis of Obstructive Sleep Apnea in an Urban Population

    PubMed Central

    Garg, Natasha; Rolle, Andrew J.; Lee, Todd A.; Prasad, Bharati

    2014-01-01

    Study Objectives: Home-based diagnosis of obstructive sleep apnea (OSA) with portable monitoring (PM) is increasingly utilized, but remains understudied in underserved and minority populations. We tested the feasibility of home PM in an urban population at risk for OSA compared to in-laboratory polysomnography (PSG) and examined patient preference with respect to home PM versus PSG. Methods: Randomized crossover study of home PM (WatchPAT200) and in-laboratory simultaneous PSG and PM in 75 urban African Americans with high pre-test probability of OSA, identified with the Berlin questionnaire. Results: Fifty-seven of 75 participants were women, average age 45 ± 11 years (mean ± SD), 35% with ≤ high school education, and 76% with annual household income < $50,000. Technical failure rates were 5.3% for home vs. 3.1% for in-laboratory PM. There was good agreement between apnea hypopnea index on PSG; AHIPSG and AHI on home PM (mean ± 2 SD of the differences = 0.64 ± 46.5 and intraclass correlation coefficient; ICC = 0.73). The areas under the curve for the receiver-operator characteristic curves for home PM were 0.90 for AHIPSG ≥ 5, 0.95 for AHIPSG ≥ 10, and 0.92 for AHIPSG ≥ 15. 62/75 (82%) participants preferred home over in-laboratory testing. Conclusions: Home PM for diagnosis of OSA in a high risk urban population is feasible, accurate, and preferred by patients. As home PM may improve access to care, the cost-effectiveness of this diagnostic strategy for OSA should be examined in underserved urban and rural populations. Clinical Trials Registration: ClinicalTrials.gov, identifier: NCT01997723 Citation: Garg N, Rolle AJ, Lee TA, Prasad B. Home-based diagnosis of obstructive sleep apnea in an urban population. J Clin Sleep Med 2014;10(8):879-885. PMID:25126034

  13. Parasite-based malaria diagnosis: Are Health Systems in Uganda equipped enough to implement the policy?

    PubMed Central

    2012-01-01

    Background Malaria case management is a key strategy for malaria control. Effective coverage of parasite-based malaria diagnosis (PMD) remains limited in malaria endemic countries. This study assessed the health system's capacity to absorb PMD at primary health care facilities in Uganda. Methods In a cross sectional survey, using multi-stage cluster sampling, lower level health facilities (LLHF) in 11 districts in Uganda were assessed for 1) tools, 2) skills, 3) staff and infrastructure, and 4) structures, systems and roles necessary for the implementing of PMD. Results Tools for PMD (microscopy and/or RDTs) were available at 30 (24%) of the 125 LLHF. All LLHF had patient registers and 15% had functional in-patient facilities. Three months’ long stock-out periods were reported for oral and parenteral quinine at 39% and 47% of LLHF respectively. Out of 131 health workers interviewed, 86 (66%) were nursing assistants; 56 (43%) had received on-job training on malaria case management and 47 (36%) had adequate knowledge in malaria case management. Overall, only 18% (131/730) Ministry of Health approved staff positions were filled by qualified personnel and 12% were recruited or transferred within six months preceding the survey. Of 186 patients that received referrals from LLHF, 130(70%) had received pre-referral anti-malarial drugs, none received pre-referral rectal artesunate and 35% had been referred due to poor response to antimalarial drugs. Conclusion Primary health care facilities had inadequate human and infrastructural capacity to effectively implement universal parasite-based malaria diagnosis. The priority capacity building needs identified were: 1) recruitment and retention of qualified staff, 2) comprehensive training of health workers in fever management, 3) malaria diagnosis quality control systems and 4) strengthening of supply chain, stock management and referral systems. PMID:22920954

  14. A comparison of liquid-based cytology with conventional Papanicolaou smears in cervical dysplasia diagnosis

    PubMed Central

    Haghighi, Fatemeh; Ghanbarzadeh, Nahid; Ataee, Marziee; Sharifzadeh, Gholamreza; Mojarrad, Javid Shahbazi; Najafi-Semnani, Fatemeh

    2016-01-01

    Background: Due to the high number of women affected by cervical cancer and the importance of an early diagnosis, combined with the frequent incidence of false-negative Papanicolaou (Pap) smear screening results for this disease, several studies have been conducted in recent years in order to find better tests. Liquid-based cytology (LBC) tests, including the liquid-based thin layer method, have demonstrated the highest potential for reducing false-negative cases and improved sample quality. This study aimed to compare the strength of the Pap smear test with fluid cytology and conventional tests in detecting cervical dysplasia. Materials and Methods: This descriptive-analytic study was conducted on 366 women who attended private laboratories for a Pap smear. The Pap smear sampling was conducted simultaneously using two methods: conventional Pap (CP) smear and LBC), from the cervix. Results: The mean age of the participants was 32 ± 8.8 years. Diagnostic results of endocervical cells, epithelial cells, vaginitis cells, and metaplastia were consistent with both conventional and liquid cytology smears, and the kappa coefficient was determined to be significant (P < 0.001). In total, 40.5% of diagnostic cases indicated bacterial inflammation 80.3% of the diagnoses in both methods were P1 and 3.9% of cases diagnosed were P2, the overall diagnostic consistency was 83.9% between the two sampling methods. The inflammation diagnosis was 40.5% and this was consistent in both methods of LBC and CP. There was one case of a false-negative diagnosis in the LBC method and 14 cases in the CP method. Conclusion: Results showed that the LBC may improve the sample's quality and reduce the number of unsatisfactory cases more than with the CP method. PMID:27995101

  15. Deep ensemble learning of sparse regression models for brain disease diagnosis.

    PubMed

    Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

    2017-04-01

    Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.

  16. Development and Evaluation of Reference Standards for Image-based Telemedicine Diagnosis and Clinical Research Studies in Ophthalmology

    PubMed Central

    Ryan, Michael C.; Ostmo, Susan; Jonas, Karyn; Berrocal, Audina; Drenser, Kimberly; Horowitz, Jason; Lee, Thomas C.; Simmons, Charles; Martinez-Castellanos, Maria-Ana; Chan, R.V. Paul; Chiang, Michael F.

    2014-01-01

    Information systems managing image-based data for telemedicine or clinical research applications require a reference standard representing the correct diagnosis. Accurate reference standards are difficult to establish because of imperfect agreement among physicians, and discrepancies between clinical vs. image-based diagnosis. This study is designed to describe the development and evaluation of reference standards for image-based diagnosis, which combine diagnostic impressions of multiple image readers with the actual clinical diagnoses. We show that agreement between image reading and clinical examinations was imperfect (689 [32%] discrepancies in 2148 image readings), as was inter-reader agreement (kappa 0.490-0.652). This was improved by establishing an image-based reference standard defined as the majority diagnosis given by three readers (13% discrepancies with image readers). It was further improved by establishing an overall reference standard that incorporated the clinical diagnosis (10% discrepancies with image readers). These principles of establishing reference standards may be applied to improve robustness of real-world systems supporting image-based diagnosis. PMID:25954463

  17. Development and Evaluation of Reference Standards for Image-based Telemedicine Diagnosis and Clinical Research Studies in Ophthalmology.

    PubMed

    Ryan, Michael C; Ostmo, Susan; Jonas, Karyn; Berrocal, Audina; Drenser, Kimberly; Horowitz, Jason; Lee, Thomas C; Simmons, Charles; Martinez-Castellanos, Maria-Ana; Chan, R V Paul; Chiang, Michael F

    2014-01-01

    Information systems managing image-based data for telemedicine or clinical research applications require a reference standard representing the correct diagnosis. Accurate reference standards are difficult to establish because of imperfect agreement among physicians, and discrepancies between clinical vs. image-based diagnosis. This study is designed to describe the development and evaluation of reference standards for image-based diagnosis, which combine diagnostic impressions of multiple image readers with the actual clinical diagnoses. We show that agreement between image reading and clinical examinations was imperfect (689 [32%] discrepancies in 2148 image readings), as was inter-reader agreement (kappa 0.490-0.652). This was improved by establishing an image-based reference standard defined as the majority diagnosis given by three readers (13% discrepancies with image readers). It was further improved by establishing an overall reference standard that incorporated the clinical diagnosis (10% discrepancies with image readers). These principles of establishing reference standards may be applied to improve robustness of real-world systems supporting image-based diagnosis.

  18. Detection and Modeling of High-Dimensional Thresholds for Fault Detection and Diagnosis

    NASA Technical Reports Server (NTRS)

    He, Yuning

    2015-01-01

    Many Fault Detection and Diagnosis (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like oil pressure too high, analog sensor values need to be discretized using a suitablethreshold. Time series of analog and discrete sensor readings are processed and discretized as they come in. This task isusually performed by the wrapper code'' of the FDD system, together with signal preprocessing and filtering. In practice,selecting the right threshold is very difficult, because it heavily influences the quality of diagnosis. If a threshold causesthe alarm trigger even in nominal situations, false alarms will be the consequence. On the other hand, if threshold settingdoes not trigger in case of an off-nominal condition, important alarms might be missed, potentially causing hazardoussituations. In this paper, we will in detail describe the underlying statistical modeling techniques and algorithm as well as the Bayesian method for selecting the most likely shape and its parameters. Our approach will be illustrated by several examples from the Aerospace domain.

  19. Quantitative Diagnosis of Tongue Cancer from Histological Images in an Animal Model

    PubMed Central

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo Georgia; Fei, Baowei

    2016-01-01

    We developed a chemically-induced oral cancer animal model and a computer aided method for tongue cancer diagnosis. The animal model allows us to monitor the progress of the lesions over time. Tongue tissue dissected from mice was sent for histological processing. Representative areas of hematoxylin and eosin (H&E) stained tissue from tongue sections were captured for classifying tumor and non-tumor tissue. The image set used in this paper consisted of 214 color images (114 tumor and 100 normal tissue samples). A total of 738 color, texture, morphometry and topology features were extracted from the histological images. The combination of image features from epithelium tissue and its constituent nuclei and cytoplasm has been demonstrated to improve the classification results. With ten iteration nested cross validation, the method achieved an average sensitivity of 96.5% and a specificity of 99% for tongue cancer detection. The next step of this research is to apply this approach to human tissue for computer aided diagnosis of tongue cancer. PMID:27656036

  20. Quantitative diagnosis of tongue cancer from histological images in an animal model

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo G.; Fei, Baowei

    2016-03-01

    We developed a chemically-induced oral cancer animal model and a computer aided method for tongue cancer diagnosis. The animal model allows us to monitor the progress of the lesions over time. Tongue tissue dissected from mice was sent for histological processing. Representative areas of hematoxylin and eosin stained tissue from tongue sections were captured for classifying tumor and non-tumor tissue. The image set used in this paper consisted of 214 color images (114 tumor and 100 normal tissue samples). A total of 738 color, texture, morphometry and topology features were extracted from the histological images. The combination of image features from epithelium tissue and its constituent nuclei and cytoplasm has been demonstrated to improve the classification results. With ten iteration nested cross validation, the method achieved an average sensitivity of 96.5% and a specificity of 99% for tongue cancer detection. The next step of this research is to apply this approach to human tissue for computer aided diagnosis of tongue cancer.

  1. Towards the Neuropsychological Diagnosis of Alzheimer's Disease: A Hybrid Model in Decision Making

    NASA Astrophysics Data System (ADS)

    de Castro, Ana Karoline Araujo; Pinheiro, Placido Rogerio; Pinheiro, Mirian Caliope Dantas

    Dementias are syndromes described by a decline in memory and other neuropsychological changes especially occurring in the elderly and increasing exponentially in function of age. Due to this fact and the therapeutical limitations in the most advanced stage of the disease, diagnosis of Alzheimer's disease is extremely important and it can provide better life conditions to patients and their families. This work presents a hybrid model, combining Influence Diagrams and the Multicriteria Method, for aiding to discover, from a battery of tests, which are the most attractive questions, in relation to the stages of CDR (Clinical Dementia Rating) in decision making for the diagnosis of Alzheimer's disease. This disease is the most common dementia. Influence Diagram is implemented using GeNie tool. Next, the judgment matrixes are constructed to obtain cardinal value scales which are implemented through MACBETH Multicriteria Methodology. The modeling and evaluation processes were carried out through a battery of standardized assessments for the evaluation of cases with Alzheimer's disease developed by Consortium to Establish a Registry for Alzheimer's disease (CERAD).

  2. Sketch-based geologic modeling

    NASA Astrophysics Data System (ADS)

    Rood, M. P.; Jackson, M.; Hampson, G.; Brazil, E. V.; de Carvalho, F.; Coda, C.; Sousa, M. C.; Zhang, Z.; Geiger, S.

    2015-12-01

    Two-dimensional (2D) maps and cross-sections, and 3D conceptual models, are fundamental tools for understanding, communicating and modeling geology. Yet geologists lack dedicated and intuitive tools that allow rapid creation of such figures and models. Standard drawing packages produce only 2D figures that are not suitable for quantitative analysis. Geologic modeling packages can produce 3D models and are widely used in the groundwater and petroleum communities, but are often slow and non-intuitive to use, requiring the creation of a grid early in the modeling workflow and the use of geostatistical methods to populate the grid blocks with geologic information. We present an alternative approach to rapidly create figures and models using sketch-based interface and modelling (SBIM). We leverage methods widely adopted in other industries to prototype complex geometries and designs. The SBIM tool contains built-in geologic rules that constrain how sketched lines and surfaces interact. These rules are based on the logic of superposition and cross-cutting relationships that follow from rock-forming processes, including deposition, deformation, intrusion and modification by diagenesis or metamorphism. The approach allows rapid creation of multiple, geologically realistic, figures and models in 2D and 3D using a simple, intuitive interface. The user can sketch in plan- or cross-section view. Geologic rules are used to extrapolate sketched lines in real time to create 3D surfaces. Quantitative analysis can be carried our directly on the models. Alternatively, they can be output as simple figures or imported directly into other modeling tools. The software runs on a tablet PC and can be used in a variety of settings including the office, classroom and field. The speed and ease of use of SBIM enables multiple interpretations to be developed from limited data, uncertainty to be readily appraised, and figures and models to be rapidly updated to incorporate new data or concepts.

  3. Optical and dielectric sensors based on antimicrobial peptides for microorganism diagnosis.

    PubMed

    Silva, Rafael R; Avelino, Karen Y P S; Ribeiro, Kalline L; Franco, Octavio L; Oliveira, Maria D L; Andrade, Cesar A S

    2014-01-01

    Antimicrobial peptides (AMPs) are natural compounds isolated from a wide variety of organisms that include microorganisms, insects, amphibians, plants, and humans. These biomolecules are considered as part of the innate immune system and are known as natural antibiotics, presenting a broad spectrum of activities against bacteria, fungi, and/or viruses. Technological innovations have enabled AMPs to be utilized for the development of novel biodetection devices. Advances in nanotechnology, such as the synthesis of nanocomposites, nanoparticles, and nanotubes have permitted the development of nanostructured platforms with biocompatibility and greater surface areas for the immobilization of biocomponents, arising as additional tools for obtaining more efficient biosensors. Diverse AMPs have been used as biological recognition elements for obtaining biosensors with more specificity and lower detection limits, whose analytical response can be evaluated through electrochemical impedance and fluorescence spectroscopies. AMP-based biosensors have shown potential for applications such as supplementary tools for conventional diagnosis methods of microorganisms. In this review, conventional methods for microorganism diagnosis as well new strategies using AMPs for the development of impedimetric and fluorescent biosensors are highlighted. AMP-based biosensors show promise as methods for diagnosing infections and bacterial contaminations as well as applications in quality control for clinical analyses and microbiological laboratories.

  4. Cardiovascular disease and diagnosis of amyotrophic lateral sclerosis: A population based study.

    PubMed

    Kioumourtzoglou, Marianthi-Anna; Seals, Ryan M; Gredal, Ole; Mittleman, Murray A; Hansen, Johnni; Weisskopf, Marc G

    Amyotrophic lateral sclerosis (ALS) is a rapidly fatal neurodegenerative disease of unknown etiology. We investigated the association between ALS diagnosis and prior cardiovascular disease (CVD), and CVD-specific, hospital admissions in the Danish population. We conducted a population based nested case-control study, including 3182 Danish residents diagnosed with ALS at age ≥20 years (1982-2009) and 100 randomly selected controls for each case, matched on age, gender and vital status. We estimated odds ratios (OR) associated with CVD, and CVD-specific hospital admissions, adjusting for socioeconomic and marital status, region of residence and past diabetes and obesity diagnoses. The estimated adjusted OR for any CVD admission at least three years prior to the date of ALS diagnosis was 1.15 (95% CI 1.04-1.27). Our results varied across cause-specific admissions; for atherosclerosis the OR was 1.36 (95% CI 1.02-1.80) and for ischemic heart disease 1.14 (95% CI 0.99-1.31), while we observed no association with hypertensive and cerebrovascular diseases. Adjusting for or stratifying by COPD status, a cigarette-smoking correlate, did not change our results. In conclusion, in our population based study we found evidence for a moderately elevated association with CVD that was stronger for specific conditions, such as atherosclerosis. Our findings may have important implications for ALS pathogenesis.

  5. Automated Diagnosis of Epilepsy using Key-point Based Local Binary Pattern of EEG Signals.

    PubMed

    Tiwari, Ashwani; Pachori, Ram Bilas; Kanhangad, Vivek; Panigrahi, Bijaya

    2016-07-11

    The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG based automated diagnosis of epilepsy. Our method involves detection of key-points at multiple scales in EEG signals using a pyramid of difference of Gaussian (DoG) filtered signals. Local binary patterns (LBP) are computed at these key-points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for classification of EEG signals. The proposed methodology has been investigated for the four wellknown classification problems namely, (i) normal and epileptic seizure, (ii) epileptic seizure and seizure-free, and (iii) normal, epileptic seizure, and seizure-free, (iv) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for classification of the above mentioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on LBP computed at key-points is simple and easy to implement for realtime epileptic seizure detection.

  6. [Importance of the interdisciplinary, evidence-based diagnosis of polycystic ovary syndrome].

    PubMed

    Gődény, Sándor; Csenteri, Orsolya

    2014-07-27

    Polycystic ovary syndrome is recognized as the most common hormonal and metabolic disorder of women. This heterogeneous endocrinopathy characterized by clinical and/or biochemical hyperandrogenism, oligo- or amenorrhoea, anovulatory infertility, and polycystic ovarian morphology. The prevalence, clinical feature and the risk of co-morbidity vary depending on the accuracy of the diagnosis and the criteria used. Evidence suggests that those women are at high risk who fulfil the criteria based on National Institute of Health. The complex feature of the syndrome and the considerable practice heterogeneity that is present with regards to diagnostic testing of patients who are suspected to have polycystic ovary syndrome require an interdisciplinary, evidence-based diagnostic approach. Such a method can ensure the patient safety and the effectiveness and efficiency of the diagnosis. This paper summarises the highest available evidence provided by well-designed studies, meta-analysis and systematic reviews of the clinical feature and the clinical implications of the diagnostic criteria of polycystic ovary syndrome.

  7. Sample pretreatment and nucleic acid-based detection for fast diagnosis utilizing microfluidic systems.

    PubMed

    Wang, Jung-Hao; Wang, Chih-Hung; Lee, Gwo-Bin

    2012-06-01

    Recently, micro-electro-mechanical-systems (MEMS) technology and micromachining techniques have enabled miniaturization of biomedical devices and systems. Not only do these techniques facilitate the development of miniaturized instrumentation for biomedical analysis, but they also open a new era for integration of microdevices for performing accurate and sensitive diagnostic assays. A so-called "micro-total-analysis-system", which integrates sample pretreatment, transport, reaction, and detection on a small chip in an automatic format, can be realized by combining functional microfluidic components manufactured by specific MEMS technologies. Among the promising applications using microfluidic technologies, nucleic acid-based detection has shown considerable potential recently. For instance, micro-polymerase chain reaction chips for rapid DNA amplification have attracted considerable interest. In addition, microfluidic devices for rapid sample pretreatment prior to nucleic acid-based detection have also achieved significant progress in the recent years. In this review paper, microfluidic systems for sample preparation, nucleic acid amplification and detection for fast diagnosis will be reviewed. These microfluidic devices and systems have several advantages over their large-scale counterparts, including lower sample/reagent consumption, lower power consumption, compact size, faster analysis, and lower per unit cost. The development of these microfluidic devices and systems may provide a revolutionary platform technology for fast sample pretreatment and accurate, sensitive diagnosis.

  8. [A new method of tremor diagnosis based on singular value decomposition of EMD].

    PubMed

    Ai, Lingmei; Wang, Jue

    2009-12-01

    Aiming at three kinds of tremor, including essential tremor (ET), Parkinsonian disease (PD) tremor and physiological tremor (PT), which are subjected to frequent clinical misdiagnosis, a new method based on singular value decomposition (SVD) of empirical mode decomposition (EMD) and support vector machine (SVM) for the recognition of tremor is proposed in this paper. First, the hand acceleration signals of three different types of 40 tremor voluntary subjects were collected on the basis of informed consent, and the EMD method was used to decompose the signals into a number of stationary intrinsic mode functions (IMFs). Then the preceding four IMFs which could describe signals were selected, and the initial feature vector matrixes were formed. After the application of SVD technique to the initial feature vector matrixes, the singular values were obtained and used as the feature vectors of tremor types to be put in the support vector machine classifier as well as in the identification of tremor types. The results of experiment have shown that the proposed diagnosis method based on SVD of EMD and SVM can extract tremor features effectively and identify tremor types accurately. It also provides a new assistant approach for clinical diagnosis of tremor.

  9. Evidence-based consensus on the diagnosis, prevention and management of hepatitis C virus disease

    PubMed Central

    Shaheen, Mahrukh Akbar; Idrees, Muhammad

    2015-01-01

    Hepatitis C virus (HCV) is a potent human pathogen and is one of the main causes of chronic hepatitis round the world. The present review describes the evidence-based consensus on the diagnosis, prevention and management of HCV disease. Various techniques, for the detection of anti-HCV immunoglobulin G immunoassays, detection of HCV RNA by identifying virus-specific molecules nucleic acid testings, recognition of core antigen for diagnosis of HCV, quantitative antigen assay, have been used to detect HCV RNA and core antigen. Advanced technologies such as nanoparticle-based diagnostic assays, loop-mediated isothermal amplification and aptamers and Ortho trak-C assay have also come to the front that provides best detection results with greater ease and specificity for detection of HCV. It is of immense importance to prevent this infection especially among the sexual partners, injecting drug users, mother-to-infant transmission of HCV, household contact, healthcare workers and people who get tattoos and piercing on their skin. Management of this infection is intended to eradicate it out of the body of patients. Management includes examining the treatment (efficacy and protection), assessment of hepatic condition before commencing therapy, controlling the parameters upon which dual and triple therapies work, monitoring the body after treatment and adjusting the co-factors. Examining the treatment in some special groups of people (HIV/HCV co-infected, hemodialysis patients, renal transplanted patients). PMID:25848486

  10. Amplification-Based DNA Analysis in the Diagnosis of Prosthetic Joint Infection

    PubMed Central

    Vandercam, Bernard; Jeumont, Sabine; Cornu, Olivier; Yombi, Jean-Cyr; Lecouvet, Frédéric; Lefèvre, Philippe; Irenge, Léonid M.; Gala, Jean-Luc

    2008-01-01

    Microbiological cultures are moderately sensitive for diagnosing prosthetic joint infection (PJI). This study was conducted to determine whether amplification-based DNA methods applied on intraoperative samples could enhance PJI diagnosis compared with culture alone in routine surgical practice. Revision arthroplasty was performed for suspected PJI (n = 41) and osteoarthrosis control (n = 28) patients, and a diagnosis of PJI was confirmed in 34 patients. Amplification by polymerase chain reaction was performed on both 16S ribosomal DNA universal target genes and femA Staphylococcus-specific target genes. Species identification was achieved through amplicon sequencing. Amplification of the femA gene led to subsequent testing for methicillin resistance by amplification of the mecA gene. Microbiological and molecular assays identified a causative organism in 22 of 34 patients (64.7%) and in 31 of 34 patients (91.2%), respectively. In 18 of the 22 culture-positive patients, molecular and microbiological results were concordant for bacterial genus, species, and/or methicillin resistance. Bacterial agents were identified only by molecular methods in nine PJI patients, including seven who were receiving antibiotics at the time of surgery and one with recent but not concomitant antibiotherapy. DNA-based methods were found to effectively complement microbiological methods, without interfering with existing procedures for sample collection, for the identification of causative pathogens from intraoperative PJI samples, especially in patients with recent or concomitant antibiotherapy. PMID:18832459

  11. Optical and dielectric sensors based on antimicrobial peptides for microorganism diagnosis

    PubMed Central

    Silva, Rafael R.; Avelino, Karen Y. P. S.; Ribeiro, Kalline L.; Franco, Octavio L.; Oliveira, Maria D. L.; Andrade, Cesar A. S.

    2014-01-01

    Antimicrobial peptides (AMPs) are natural compounds isolated from a wide variety of organisms that include microorganisms, insects, amphibians, plants, and humans. These biomolecules are considered as part of the innate immune system and are known as natural antibiotics, presenting a broad spectrum of activities against bacteria, fungi, and/or viruses. Technological innovations have enabled AMPs to be utilized for the development of novel biodetection devices. Advances in nanotechnology, such as the synthesis of nanocomposites, nanoparticles, and nanotubes have permitted the development of nanostructured platforms with biocompatibility and greater surface areas for the immobilization of biocomponents, arising as additional tools for obtaining more efficient biosensors. Diverse AMPs have been used as biological recognition elements for obtaining biosensors with more specificity and lower detection limits, whose analytical response can be evaluated through electrochemical impedance and fluorescence spectroscopies. AMP-based biosensors have shown potential for applications such as supplementary tools for conventional diagnosis methods of microorganisms. In this review, conventional methods for microorganism diagnosis as well new strategies using AMPs for the development of impedimetric and fluorescent biosensors are highlighted. AMP-based biosensors show promise as methods for diagnosing infections and bacterial contaminations as well as applications in quality control for clinical analyses and microbiological laboratories. PMID:25191319

  12. Development of a FPGA based fuzzy neural network system for early diagnosis of critical health condition of a patient.

    PubMed

    Chowdhury, Shubhajit Roy; Saha, Hiranmay

    2010-02-01

    The paper describes the design and training of a fuzzy neural network used for early diagnosis of a patient through an FPGA based implementation of a smart instrument. The system employs a fuzzy interface cascaded with a feed-forward neural network. In order to obtain an optimum decision regarding the future pathophysiological state of a patient, the optimal weights of the synapses between the neurons have been determined by using inverse delayed function model of neurons. The neurons that are considered in the proposed network are devoid of self connections instead of commonly used self connected neurons. The current work also find out the optimal number of neurons in the hidden layer for accurate diagnosis as against the available number of CLB in the FPGA. The system has been trained and tested with renal data of patients taken at 10 days interval of time. Applying the methodology, the chance of attainment of critical renal condition of a patient has been predicted with an accuracy of 95.2%, 30 days ahead of actually attaining the critical condition. The system has also been tested for pathophysiological state prediction of patients at multiple time steps ahead and the prediction at the next instant of time stands out to be the most accurate.

  13. Tetralogy of Fallot Cardiac Function Evaluation and Intelligent Diagnosis Based on Dual-Source Computed Tomography Cardiac Images.

    PubMed

    Cai, Ken; Rongqian, Yang; Li, Lihua; Xie, Zi; Ou, Shanxing; Chen, Yuke; Dou, Jianhong

    2016-05-01

    Tetralogy of Fallot (TOF) is the most common complex congenital heart disease (CHD) of the cyanotic type. Studies on ventricular functions have received an increasing amount of attention as the development of diagnosis and treatment technology for CHD continues to advance. Reasonable options for imaging examination and accurate assessment of preoperative and postoperative left ventricular functions of TOF patients are important in improving the cure rate of TOF radical operation, therapeutic evaluation, and judgment prognosis. Therefore, with the aid of dual-source computed tomography (DSCT), cardiac images with high temporal resolution and high definition, we measured the left ventricular time-volume curve using image data and calculating the left ventricular function parameters to conduct the preliminary evaluation on TOF patients. To comprehensively evaluate the cardiac function, the segmental ventricular wall function parameters were measured, and the measurement results were mapped to a bull's eye diagram to realize the standardization of segmental ventricular wall function evaluation. Finally, we introduced a new clustering method based on auto-regression model parameters and combined this method with Euclidean distance measurements to establish an intelligent diagnosis of TOF. The results of this experiment show that the TOF evaluation and the intelligent diagnostic methods proposed in this article are feasible.

  14. Consistency of Cluster Analysis for Cognitive Diagnosis: The Reduced Reparameterized Unified Model and the General Diagnostic Model.

    PubMed

    Chiu, Chia-Yi; Köhn, Hans-Friedrich

    2016-09-01

    The asymptotic classification theory of cognitive diagnosis (ACTCD) provided the theoretical foundation for using clustering methods that do not rely on a parametric statistical model for assigning examinees to proficiency classes. Like general diagnostic classification models, clustering methods can be useful in situations where the true diagnostic classification model (DCM) underlying the data is unknown and possibly misspecified, or the items of a test conform to a mix of multiple DCMs. Clustering methods can also be an option when fitting advanced and complex DCMs encounters computational difficulties. These can range from the use of excessive CPU times to plain computational infeasibility. However, the propositions of the ACTCD have only been proven for the Deterministic Input Noisy Output "AND" gate (DINA) model and the Deterministic Input Noisy Output "OR" gate (DINO) model. For other DCMs, there does not exist a theoretical justification to use clustering for assigning examinees to proficiency classes. But if clustering is to be used legitimately, then the ACTCD must cover a larger number of DCMs than just the DINA model and the DINO model. Thus, the purpose of this article is to prove the theoretical propositions of the ACTCD for two other important DCMs, the Reduced Reparameterized Unified Model and the General Diagnostic Model.

  15. Building method of diagnostic model of Bayesian networks based on fault tree

    NASA Astrophysics Data System (ADS)

    Liu, Xiao; Li, Haijun; Li, Lin

    2008-10-01

    Fault tree (FT) is usually a reliability and security analysis and diagnoses decision model. It is also in common use that expressing fault diagnosis question with fault tree model. But it will not be changed easily if fault free model was built, and it could not accept and deal with new information easily. It is difficult to put the information which have nothing to do with equipment fault but can be used to fault diagnosis into diagnostic course. Bayesian Networks (BN) can learn and improve its network architecture and parameters at any time by way of practice accumulation, and raises the ability of fault diagnosis. The method of building BN based on FT is researched on this article, this method could break through the limitations of FT itself, make BN be more extensively applied to the domain of fault diagnosis and gains much better ability of fault analysis and diagnosis.

  16. a Historical Timber Frame Model for Diagnosis and Documentation Before Building Restoration

    NASA Astrophysics Data System (ADS)

    Koehl, M.; Viale, A.; Reeb, S.

    2013-09-01

    The aim of the project that is described in this paper was to define a four-level timber frame survey mode of a historical building: the so-called "Andlau's Seigniory", Alsace, France. This historical building (domain) was built in the late XVIth century and is now in a stage of renovation in order to become a heritage interpretation centre. The used measurement methods combine Total Station measurements, Photogrammetry and 3D Terrestrial Laser scanner. Different modelling workflows were tested and compared according to the data acquisition method, but also according to the characteristics of the reconstructed model in terms of accuracy and level of detail. 3D geometric modelling of the entire structure was performed including modelling the degree of detail adapted to the needs. The described 3D timber framework exists now in different versions, from a theoretical and geometrical one up to a very detailed one, in which measurements and evaluation of deformation by time are potentially allowed. The virtually generated models involving archaeologists, architects, historians and specialists in historical crafts, are intended to be used during the four stages of the project: (i) knowledge of the current state of needs for diagnosis and understanding of former construction techniques; (ii) preparation and evaluation of restoration steps; (iii) knowledge and documentation concerning the archaeological object; (iv) transmission and dissemination of knowledge through the implementation of museum animations. Among the generated models we can also find a documentation of the site in the form of virtual tours created from panoramic photographs before and during the restoration works. Finally, the timber framework model was structured and integrated into a 3D GIS, where the association of descriptive and complementary digital documents was possible. Both offer tools leading to the diagnosis, the understanding of the structure, knowledge dissemination, documentation and the

  17. Two Stage Helical Gearbox Fault Detection and Diagnosis based on Continuous Wavelet Transformation of Time Synchronous Averaged Vibration Signals

    NASA Astrophysics Data System (ADS)

    Elbarghathi, F.; Wang, T.; Zhen, D.; Gu, F.; Ball, A.

    2012-05-01

    Vibration signals from a gearbox are usually very noisy which makes it difficult to find reliable symptoms of a fault in a multistage gearbox. This paper explores the use of time synchronous average (TSA) to suppress the noise and Continue Wavelet Transformation (CWT) to enhance the non-stationary nature of fault signal for more accurate fault diagnosis. The results obtained in diagnosis an incipient gear breakage show that fault diagnosis results can be improved by using an appropriate wavelet. Moreover, a new scheme based on the level of wavelet coefficient amplitudes of baseline data alone, without faulty data samples, is suggested to select an optimal wavelet.

  18. A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

    PubMed Central

    Su, Qiang; Zhang, Mo; Zhu, Yanhong; Wang, Qiugen; Wang, Qian

    2017-01-01

    Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques. PMID:28127385

  19. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic.

    PubMed

    Hou, Shi-Wang; Feng, Shunxiao; Wang, Hui

    2016-01-01

    Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

  20. Space-Based Diagnosis of Surface Ozone Sensitivity to Anthropogenic Emissions

    NASA Technical Reports Server (NTRS)

    Martin, Randall V.; Fiore, Arlene M.; VanDonkelaar, Aaron

    2004-01-01

    We present a novel capability in satellite remote sensing with implications for air pollution control strategy. We show that the ratio of formaldehyde columns to tropospheric nitrogen dioxide columns is an indicator of the relative sensitivity of surface ozone to emissions of nitrogen oxides (NO(x) = NO + NO2) and volatile organic compounds (VOCs). The diagnosis from these space-based observations is highly consistent with current understanding of surface ozone chemistry based on in situ observations. The satellite-derived ratios indicate that surface ozone is more sensitive to emissions of NO(x) than of VOCs throughout most continental regions of the Northern Hemisphere during summer. Exceptions include Los Angeles and industrial areas of Germany. A seasonal transition occurs in the fall when surface ozone becomes less sensitive to NOx and more sensitive to VOCs.

  1. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

    PubMed Central

    Feng, Shunxiao; Wang, Hui

    2016-01-01

    Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating. PMID:28058046

  2. Usefulness of Single Column Model Diagnosis through Short-Term Predictions.

    NASA Astrophysics Data System (ADS)

    Bergman, John W.; Sardeshmukh, Prashant D.

    2003-11-01

    Single column models (SCMs) provide an economical framework for developing and diagnosing representations of diabatic processes in weather and climate models. Their economy is achieved at the price of ignoring interactions with the circulation dynamics and with neighboring columns. It has recently been emphasized that this decoupling can lead to spurious error growth in SCM integrations that can totally obscure the error growth due to errors in the column physics that one hopes to isolate through such integrations. This paper suggests one way around this “existential crisis” of single column modeling. The basic idea is to focus on short-term SCM forecast errors, at ranges of 6 h or less, before a grossly unrealistic model state develops and before complex diabatic interactions render a clear diagnosis impossible.To illustrate, a short-term forecast error diagnosis of the NCAR SCM is presented for tropical conditions observed during the Tropical Ocean and Global Atmosphere (TOGA) Coupled Ocean Atmosphere Response Experiment (COARE). The 21-day observing period is divided into 84 6-h segments for this purpose. The SCM error evolution is shown to be nearly linear over these 6-h segments and, indeed, apart from a vertical mean bias, to be mainly an extrapolation of initial tendencies. The latter are then decomposed into contributions by various components of the column physics, and additional 6-h integrations are performed with each component separately and in combination with others to assess its contribution to the 6-h errors. Initial tendency and 6-h error diagnostics thus complement each other in diagnosing column physics errors by this approach.Although the SCM evolution from one time step to the next is nearly linear, the finite-amplitude adjustments made multiple times within each time step to the temperature and humidity to remove supersaturation and convective instabilities make it necessary to consider nonlinear interactions between the column physics

  3. Irritable bowel syndrome: Pathogenesis, diagnosis, treatment, and evidence-based medicine

    PubMed Central

    Saha, Lekha

    2014-01-01

    Irritable bowel syndrome (IBS) is a chronic and debilitating functional gastrointestinal disorder that affects 9%-23% of the population across the world. The percentage of patients seeking health care related to IBS approaches 12% in primary care practices and is by far the largest subgroup seen in gastroenterology clinics. It has been well documented that these patients exhibit a poorer quality of life and utilize the health care system to a greater degree than patients without this diagnosis. The pathophysiology of IBS is not clear. Many theories have been put forward, but the exact cause of IBS is still uncertain. According to the updated ROME III criteria, IBS is a clinical diagnosis and presents as one of the three predominant subtypes: (1) IBS with constipation (IBS-C); (2) IBS with diarrhea (IBS-D); and (3) mixed IBS (IBS-M); former ROME definitions refer to IBS-M as alternating IBS (IBS-A). Across the IBS subtypes, the presentation of symptoms may vary among patients and change over time. Patients report the most distressing symptoms to be abdominal pain, straining, myalgias, urgency, bloating and feelings of serious illness. The complexity and diversity of IBS presentation makes treatment difficult. Although there are reviews and guidelines for treating IBS, they focus on the efficacy of medications for IBS symptoms using high-priority endpoints, leaving those of lower priority largely unreported. Therefore, the aim of this review is to provide a comprehensive evidence-based review of the diagnosis, pathogenesis and treatment to guide clinicians diagnosing and treating their patients. PMID:24944467

  4. Inertia based microfluidic capture and characterisation of circulating tumour cells for the diagnosis of lung cancer

    PubMed Central

    Chudasama, Dimple Y.; Freydina, Daria V.; Freidin, Maxim B.; Leung, Maria; Montero Fernandez, Angeles; Rice, Alexandra; Nicholson, Andrew G.; Karteris, Emmanouil; Anikin, Vladimir

    2016-01-01

    Background Routine clinical application of circulating tumour cells (CTCs) for blood based diagnostics is yet to be established. Despite growing evidence of their clinical utility for diagnosis, prognosis and treatment monitoring, the efficacy of a robust platform and universally accepted diagnostic criteria remain uncertain. We evaluate the diagnostic performance of a microfluidic CTC isolation platform using cytomorphologic criteria in patients undergoing lung cancer surgery. Methods Blood was processed from 51 patients undergoing surgery for known or suspected lung cancer using the ClearBridge ClearCell FX systemTM (ClearBridge Biomedics, Singapore). Captured cells were stained on slides with haematoxylin and eosin (H&E) and independently assessed by two pathologist teams. Diagnostic performance was evaluated against the pathologists reported diagnosis of cancer from surgically obtained specimens. Results Cancer was diagnosed in 43.1% and 54.9% of all cases. In early stage primary lung cancer, between the two reporting teams, a positive diagnosis of CTCs was made for 50% and 66.7% of patients. The agreement between the reporting teams was 80.4%, corresponding to a kappa-statistic of 0.61±0.11 (P<0.001), indicating substantial agreement. Sensitivity levels for the two teams were calculated as 59% (95% CI, 41–76%) and 41% (95% CI, 24–59%), with a specificity of 53% for both. Conclusions The performance of the tested microfluidic antibody independent device to capture CTCs using standard cytomorphological criteria provides the potential of a diagnostic blood test for lung cancer. PMID:28149842

  5. Irritable bowel syndrome: pathogenesis, diagnosis, treatment, and evidence-based medicine.

    PubMed

    Saha, Lekha

    2014-06-14

    Irritable bowel syndrome (IBS) is a chronic and debilitating functional gastrointestinal disorder that affects 9%-23% of the population across the world. The percentage of patients seeking health care related to IBS approaches 12% in primary care practices and is by far the largest subgroup seen in gastroenterology clinics. It has been well documented that these patients exhibit a poorer quality of life and utilize the health care system to a greater degree than patients without this diagnosis. The pathophysiology of IBS is not clear. Many theories have been put forward, but the exact cause of IBS is still uncertain. According to the updated ROME III criteria, IBS is a clinical diagnosis and presents as one of the three predominant subtypes: (1) IBS with constipation (IBS-C); (2) IBS with diarrhea (IBS-D); and (3) mixed IBS (IBS-M); former ROME definitions refer to IBS-M as alternating IBS (IBS-A). Across the IBS subtypes, the presentation of symptoms may vary among patients and change over time. Patients report the most distressing symptoms to be abdominal pain, straining, myalgias, urgency, bloating and feelings of serious illness. The complexity and diversity of IBS presentation makes treatment difficult. Although there are reviews and guidelines for treating IBS, they focus on the efficacy of medications for IBS symptoms using high-priority endpoints, leaving those of lower priority largely unreported. Therefore, the aim of this review is to provide a comprehensive evidence-based review of the diagnosis, pathogenesis and treatment to guide clinicians diagnosing and treating their patients.

  6. Matrix Failure Modes and Effects Analysis as a Knowledge Base for a Real Time Automated Diagnosis Expert System

    NASA Technical Reports Server (NTRS)

    Herrin, Stephanie; Iverson, David; Spukovska, Lilly; Souza, Kenneth A. (Technical Monitor)

    1994-01-01

    Failure Modes and Effects Analysis contain a wealth of information that can be used to create the knowledge base required for building automated diagnostic Expert systems. A real time monitoring and diagnosis expert system based on an actual NASA project's matrix failure modes and effects analysis was developed. This Expert system Was developed at NASA Ames Research Center. This system was first used as a case study to monitor the Research Animal Holding Facility (RAHF), a Space Shuttle payload that is used to house and monitor animals in orbit so the effects of space flight and microgravity can be studied. The techniques developed for the RAHF monitoring and diagnosis Expert system are general enough to be used for monitoring and diagnosis of a variety of other systems that undergo a Matrix FMEA. This automated diagnosis system was successfully used on-line and validated on the Space Shuttle flight STS-58, mission SLS-2 in October 1993.

  7. A new 3D texture feature based computer-aided diagnosis approach to differentiate pulmonary nodules

    NASA Astrophysics Data System (ADS)

    Han, Fangfang; Wang, Huafeng; Song, Bowen; Zhang, Guopeng; Lu, Hongbing; Moore, William; Zhao, Hong; Liang, Zhengrong

    2013-02-01

    To distinguish malignant pulmonary nodules from benign ones is of much importance in computer-aided diagnosis of lung diseases. Compared to many previous methods which are based on shape or growth assessing of nodules, this proposed three-dimensional (3D) texture feature based approach extracted fifty kinds of 3D textural features from gray level, gradient and curvature co-occurrence matrix, and more derivatives of the volume data of the nodules. To evaluate the presented approach, the Lung Image Database Consortium public database was downloaded. Each case of the database contains an annotation file, which indicates the diagnosis results from up to four radiologists. In order to relieve partial-volume effect, interpolation process was carried out to those volume data with image slice thickness more than 1mm, and thus we had categorized the downloaded datasets to five groups to validate the proposed approach, one group of thickness less than 1mm, two types of thickness range from 1mm to 1.25mm and greater than 1.25mm (each type contains two groups, one with interpolation and the other without). Since support vector machine is based on statistical learning theory and aims to learn for predicting future data, so it was chosen as the classifier to perform the differentiation task. The measure on the performance was based on the area under the curve (AUC) of Receiver Operating Characteristics. From 284 nodules (122 malignant and 162 benign ones), the validation experiments reported a mean of 0.9051 and standard deviation of 0.0397 for the AUC value on average over 100 randomizations.

  8. Effect of health belief model and health promotion model on breast cancer early diagnosis behavior: a systematic review.

    PubMed

    Ersin, Fatma; Bahar, Zuhal

    2011-01-01

    Breast cancer is an important public health problem on the grounds that it is frequently seen and it is a fatal disease. The objective of this systematic analysis is to indicate the effects of interventions performed by nurses by using the Health Belief Model (HBM) and Health Promotion Model (HPM) on the breast cancer early diagnosis behaviors and on the components of the Health Belief Model and Health Promotion Model. The reveiw was created in line with the Centre for Reviews and Dissemination guide dated 2009 (CRD) and developed by York University National Institute of Health Researches. Review was conducted by using PUBMED, OVID, EBSCO and COCHRANE databases. Six hundred seventy eight studies (PUBMED: 236, OVID: 162, EBSCO: 175, COCHRANE:105) were found in total at the end of the review. Abstracts and full texts of these six hundred seventy eight studies were evaluated in terms of inclusion and exclusion criteria and 9 studies were determined to meet the criteria. Samplings of the studies varied between ninety four and one thousand six hundred fifty five. It was detected in the studies that educations provided by taking the theories as basis became effective on the breast cancer early diagnosis behaviors. When the literature is examined, it is observed that the experimental researches which compare the concepts of Health Belief Model (HBM) and Health Promotion Model (HPM) preoperatively and postoperatively and show the effect of these concepts on education and are conducted by nurses are limited in number. Randomized controlled studies which compare HBM and HPM concepts preoperatively and postoperatively and show the efficiency of the interventions can be useful in evaluating the efficiency of the interventions.

  9. Forensic diagnosis of ante- and postmortem burn based on aquaporin-3 gene expression in the skin.

    PubMed

    Kubo, Hidemichi; Hayashi, Takahito; Ago, Kazutoshi; Ago, Mihoko; Kanekura, Takuro; Ogata, Mamoru

    2014-05-01

    In order to diagnose death associated with fire, it is essential to show that the person was exposed to heat while still alive. We investigated both AQP1 and AQP3 expression in the skin of an experimental burn model, as well as in forensic autopsy cases, and discuss its role in the differential diagnosis of ante- and postmortem burns. In animal experiments, there was no difference in AQP1 gene expression among four groups (n=4): antemortem burn, postmortem burn, mechanical wound, and control. However, AQP3 expression in the antemortem burn was increased significantly compared with that of the other groups even at 5min after burn. Water content of the skin was decreased significantly by the burn procedure. Consistent with animal experiments, AQP3 gene expression in the skin of antemortem burn cases was increased significantly compared with postmortem burns, mechanical wounds, and controls (n=12 in each group). These observations suggest that dermal AQP3 gene expression was increased to maintain water homeostasis in response to dehydration from burn. Finally, our results suggest that AQP3 gene expression may be useful for forensic molecular diagnosis of antemortem burn.

  10. Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology.

    PubMed

    Cavalli, Fabio; Lusnig, Luca; Trentin, Edmondo

    2016-08-29

    Sex determination on skeletal remains is one of the most important diagnosis in forensic cases and in demographic studies on ancient populations. Our purpose is to realize an automatic operator-independent method to determine the sex from the bone shape and to test an intelligent, automatic pattern recognition system in an anthropological domain. Our multiple-classifier system is based exclusively on the morphological variants of a curve that represents the sagittal profile of the calvarium, modeled via artificial neural networks, and yields an accuracy higher than 80 %. The application of this system to other bone profiles is expected to further improve the sensibility of the methodology.

  11. Fault detection, diagnosis, and data-driven modeling in HVAC chillers

    NASA Astrophysics Data System (ADS)

    Namburu, Setu M.; Luo, Jianhui; Azam, Mohammad; Choi, Kihoon; Pattipati, Krishna R.

    2005-05-01

    Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and fault diagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and most of the schemes are systemspecific. A generic real-time FDD scheme, applicable to all possible operating conditions, can significantly reduce HVAC equipment downtime, thus improving the efficiency of building energy management systems. This paper presents a FDD methodology for faults in centrifugal chillers. The FDD scheme compares the diagnostic performance of three data-driven techniques, namely support vector machines (SVM), principal component analysis (PCA), and partial least squares (PLS). In addition, a nominal model of a chiller that can predict system response under new operating conditions is developed using PLS. We used the benchmark data on a 90-ton real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables under nominal and eight fault conditions of different severities at twenty seven operating modes.

  12. Potential model for differential diagnosis between Crohn's disease and primary intestinal lymphoma

    PubMed Central

    Zhang, Tian-Yu; Lin, Yun; Fan, Rong; Hu, Shu-Rong; Cheng, Meng-Meng; Zhang, Mao-Chen; Hong, Li-Wen; Zhou, Xiao-Lin; Wang, Zheng-Ting; Zhong, Jie

    2016-01-01

    AIM To evaluate the usefulness of different parameters to differentiate Crohn’s disease (CD) from primary intestinal lymphoma (PIL). METHODS The medical records of 85 patients with CD and 56 patients with PIL were reviewed retrospectively. Demographic, clinical, laboratory, endoscopic, and computed tomographic enterography (CTE) parameters were collected. The univariate value of each parameter was analyzed. A differentiation model was established by pooling all the valuable parameters. Diagnostic efficacy was analyzed, and a receiver operating characteristic (ROC) curve was plotted. RESULTS The demographic and clinical parameters that showed significant values for differentiating CD from PIL included age of onset, symptom duration, presence of diarrhea, abdominal mass, and perianal lesions (P < 0.05). Elevated lactate dehydrogenase and serum β2-microglobulin levels suggested a PIL diagnosis (P < 0.05). The endoscopic parameters that showed significant values for differentiating CD from PIL included multiple-site lesions, longitudinal ulcer, irregular ulcer, and intraluminal proliferative mass (P < 0.05). The CTE parameters that were useful in the identification of the two conditions included involvement of ≤ 3 segments, circular thickening of the bowel wall, wall thickness > 8 mm, aneurysmal dilation, stricture with proximal dilation, “comb sign”, mass showing the “sandwich sign”, and intussusceptions (P < 0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the differentiation model were 91.8%, 96.4%, 93.6%, 97.5%, and 88.5%, respectively. The cutoff value was 0.5. The area under the ROC curve was 0.989. CONCLUSION The differentiation model that integrated the various parameters together may yield a high diagnostic efficacy in the differential diagnosis between CD and PIL. PMID:27895429

  13. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection.

    PubMed

    Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

    2015-07-17

    Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.

  14. Differential diagnosis between mesothelioma and adenocarcinoma: a multimodal approach based on ultrastructure and immunocytochemistry

    SciTech Connect

    Bedrossian, C.W.; Bonsib, S.; Moran, C. )

    1992-05-01

    Most compensations for asbestos-related deaths secondary to cancer center around mesothelioma and bronchogenic carcinoma. The differential diagnosis between mesothelioma and adenocarcinoma is a common and troublesome one, necessitating the correlation between clinical history, radiographic findings, and pathologic examination of tissues and cells. We describe a multimodal approach based on the use of routine and special stains, immunocytochemistry, and electron microscopy for distinguishing between mesothelioma and adenocarcinoma. Once a malignant diagnosis is arrived at by careful pathological examination, the tumor is classified as mesothelioma if mesothelial cells are identified as the constituent cells of the neoplasm. Mesothelial cells are recognized by (1) their main ultrastructural features: slender and elongated microvilli, abundant intermediate filaments, and lacking secretory granules; and (2) their characteristic immunocytochemical reactivity: positivity for cytokeratin, EMA, and vimentin, and negativity for carcinoembryonic antigen (CEA), B72-3, Leu-M1, and other gland-cell markers. A variety of methods have been attempted in an effort to distinguish between reactive and malignant mesothelial cells. In practice, however, such distinction depends more on experience and expertise than in any fool-proof ancillary tests. A number of these tests are discussed along with the illustration of classical and unusual examples of mesothelioma and other pleural tumors.

  15. Evaluation and diagnosis of wrist pain: a case-based approach.

    PubMed

    Shehab, Ramsey; Mirabelli, Mark H

    2013-04-15

    Patients with wrist pain commonly present with an acute injury or spontaneous onset of pain without a definite traumatic event. A fall onto an outstretched hand can lead to a scaphoid fracture, which is the most commonly fractured carpal bone. Conventional radiography alone can miss up to 30 percent of scaphoid fractures. Specialized views (e.g., posteroanterior in ulnar deviation, pronated oblique) and repeat radiography in 10 to 14 days can improve sensitivity for scaphoid fractures. If a suspected scaphoid fracture cannot be confirmed with plain radiography, a bone scan or magnetic resonance imaging can be used. Subacute or chronic wrist pain usually develops gradually with or without a prior traumatic event. In these cases, the differential diagnosis is wide and includes tendinopathy and nerve entrapment. Overuse of the muscles of the forearm and wrist may lead to tendinopathy. Radial pain involving mostly the first extensor compartment is commonly de Quervain tenosynovitis. The diagnosis is based on history and examination findings of a positive Finkelstein test and a negative grind test. Nerve entrapment at the wrist presents with pain and also with sensory and sometimes motor symptoms. In ulnar neuropathies of the wrist, the typical presentation is wrist discomfort with sensory changes in the fourth and fifth digits. Activities that involve repetitive or prolonged wrist extension, such as cycling, karate, and baseball (specifically catchers), may increase the risk of ulnar neuropathy. Electrodiagnostic tests identify the area of nerve entrapment and the extent of the pathology.

  16. Nanomaterials for diagnosis: challenges and applications in smart devices based on molecular recognition.

    PubMed

    Oliveira, Osvaldo N; Iost, Rodrigo M; Siqueira, José R; Crespilho, Frank N; Caseli, Luciano

    2014-09-10

    Clinical diagnosis has always been dependent on the efficient immobilization of biomolecules in solid matrices with preserved activity, but significant developments have taken place in recent years with the increasing control of molecular architecture in organized films. Of particular importance is the synergy achieved with distinct materials such as nanoparticles, antibodies, enzymes, and other nanostructures, forming structures organized on the nanoscale. In this review, emphasis will be placed on nanomaterials for biosensing based on molecular recognition, where the recognition element may be an enzyme, DNA, RNA, catalytic antibody, aptamer, and labeled biomolecule. All of these elements may be assembled in nanostructured films, whose layer-by-layer nature is essential for combining different properties in the same device. Sensing can be done with a number of optical, electrical, and electrochemical methods, which may also rely on nanostructures for enhanced performance, as is the case of reporting nanoparticles in bioelectronics devices. The successful design of such devices requires investigation of interface properties of functionalized surfaces, for which a variety of experimental and theoretical methods have been used. Because diagnosis involves the acquisition of large amounts of data, statistical and computational methods are now in widespread use, and one may envisage an integrated expert system where information from different sources may be mined to generate the diagnostics.

  17. Development of a Fluorescence-Based Sensor for Rapid Diagnosis of Cyanide Exposure

    PubMed Central

    2015-01-01

    Although commonly known as a highly toxic chemical, cyanide is also an essential reagent for many industrial processes in areas such as mining, electroplating, and synthetic fiber production. The “heavy” use of cyanide in these industries, along with its necessary transportation, increases the possibility of human exposure. Because the onset of cyanide toxicity is fast, a rapid, sensitive, and accurate method for the diagnosis of cyanide exposure is necessary. Therefore, a field sensor for the diagnosis of cyanide exposure was developed based on the reaction of naphthalene dialdehyde, taurine, and cyanide, yielding a fluorescent β-isoindole. An integrated cyanide capture “apparatus”, consisting of sample and cyanide capture chambers, allowed rapid separation of cyanide from blood samples. Rabbit whole blood was added to the sample chamber, acidified, and the HCN gas evolved was actively transferred through a stainless steel channel to the capture chamber containing a basic solution of naphthalene dialdehyde (NDA) and taurine. The overall analysis time (including the addition of the sample) was <3 min, the linear range was 3.13–200 μM, and the limit of detection was 0.78 μM. None of the potential interferents investigated (NaHS, NH4OH, NaSCN, and human serum albumin) produced a signal that could be interpreted as a false positive or a false negative for cyanide exposure. Most importantly, the sensor was 100% accurate in diagnosing cyanide poisoning for acutely exposed rabbits. PMID:24383576

  18. Nondestructive diagnosis of flip chips based on vibration analysis using PCA-RBF

    NASA Astrophysics Data System (ADS)

    Su, Lei; Shi, Tielin; Liu, Zhiping; Zhou, Hongdi; Du, Li; Liao, Guanglan

    2017-02-01

    Flip chip technology combined with solder bump interconnection has been widely applied in IC package. The solder bumps are sandwiched between dies and substrates, leading to conventional techniques being difficult to diagnose the flip chips. Meanwhile, these conventional diagnosis methods are usually performed by human visual judgment. The human eye-fatigue can easily cause fault detection. Thus, it is difficult and crucial to detect the defects of flip chips automatically. In this paper, a nondestructive diagnosis system based on vibration analysis is proposed. The flip chip is excited by air-coupled ultrasounds and raw vibration signals are measured by a laser scanning vibrometer. Forty-two features are extracted for analysis, including ten time domain features, sixteen frequency domain features and sixteen wavelet packet energy features. Principal component analysis is used for feature reduction. Radial basis function neural network is adopted for classification and recognition. Flip chips in three states (good flip chips, flip chips with missing solder bumps and flip chips with open solder bumps) are utilized to validate the proposed method. The results demonstrate that this method is effective for defect inspection in flip chip package.

  19. Irritable bowel syndrome in children: Pathogenesis, diagnosis and evidence-based treatment

    PubMed Central

    Sandhu, Bhupinder Kaur; Paul, Siba Prosad

    2014-01-01

    Irritable bowel syndrome (IBS) is the commonest cause of recurrent abdominal pain (RAP) in children in both more developed and developing parts of the world. It is defined by the Rome III criteria for functional gastrointestinal disorders. It is characterized by abdominal pain that is improved by defecation and whose onset is associated with a change in stool form and or frequency and is not explained by structural or biochemical abnormalities. It is estimated that 10%-15% of older children and adolescents suffer from IBS. IBS can be considered to be a brain-gut disorder possibly due to complex interaction between environmental and hereditary factors. The diagnosis of IBS is made based on the Rome III criteria together with ruling out organic causes of RAP in children such as inflammatory bowel disease and celiac disease. Once the diagnosis of IBS is made, it is important to explain to the parents (and children) that there is no serious underlying disease. This reassurance may be effective treatment in a large number of cases. Lifestyle modifications, stress management, dietary interventions and probiotics may be beneficial in some cases. Although there is limited evidence for efficacy of pharmacological therapies such as antispasmodics and antidiarrheals; these have a role in severe cases. Biopsychosocial therapies have shown encouraging results in initial trials but are beset by limited availability. Further research is necessary to understand the pathophysiology and provide specific focused therapies. PMID:24876724

  20. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

    PubMed Central

    Li, Qiang; Zhao, Xuehua; Cai, ZhenNao; Tong, Changfei; Liu, Wenbin; Tian, Xin

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts. PMID:28246543

  1. The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

    NASA Technical Reports Server (NTRS)

    Lee, S. C.

    1989-01-01

    The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.

  2. Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhou, Peng; Lu, Siliang; Liu, Fang; Liu, Yongbin; Li, Guihua; Zhao, Jiwen

    2017-03-01

    Stochastic resonance (SR), which is characterized by the fact that proper noise can be utilized to enhance weak periodic signals, has been widely applied in weak signal detection. SR is a nonlinear parameterized filter, and the output signal relies on the system parameters for the deterministic input signal. The most commonly used index for parameter tuning in the SR procedure is the signal-to-noise ratio (SNR). However, using the SNR index to evaluate the denoising effect of SR quantitatively is insufficient when the target signal frequency cannot be estimated accurately. To address this issue, six different indexes, namely, power spectral kurtosis of the SR output signal, correlation coefficient between the SR output and the original signal, peak SNR, structural similarity, root mean square error, and smoothness, are constructed in this study to measure the SR output quantitatively. These six quantitative indexes are fused into a new synthetic quantitative index (SQI) via a back propagation neural network to guide the adaptive parameter selection of the SR procedure. The index fusion procedure reduces the instability of each index and thus improves the robustness of parameter tuning. In addition, genetic algorithm is utilized to quickly select the optimal SR parameters. The efficiency of bearing fault diagnosis is thus further improved. The effectiveness and efficiency of the proposed SQI-based adaptive SR method for bearing fault diagnosis are verified through numerical and experiment analyses.

  3. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis

    NASA Astrophysics Data System (ADS)

    He, Shuilong; Chen, Jinglong; Zhou, Zitong; Zi, Yanyang; Wang, Yanxue; Wang, Xiaodong

    2016-08-01

    Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what's more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.

  4. Detection of signal transients based on wavelet and statistics for machine fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhu, Z. K.; Yan, Ruqiang; Luo, Liheng; Feng, Z. H.; Kong, F. R.

    2009-05-01

    This paper presents a transient detection method that combines continuous wavelet transform (CWT) and Kolmogorov-Smirnov (K-S) test for machine fault diagnosis. According to this method, the CWT represents the signal in the time-scale plane, and the proposed "step-by-step detection" based on K-S test identifies the transient coefficients. Simulation study shows that the transient feature can be effectively identified in the time-scale plane with the K-S test. Moreover, the transients can be further transformed back into the time domain through the inverse CWT. The proposed method is then utilized in the gearbox vibration transient detection for fault diagnosis, and the results show that the transient features both expressed in the time-scale plane and re-constructed in the time domain characterize the gearbox condition and fault severity development more clearly than the original time domain signal. The proposed method is also applied to the vibration signals of cone bearings with the localized fault in the inner race, outer race and the rolling elements, respectively. The detected transients indicate not only the existence of the bearing faults, but also the information about the fault severity to a certain degree.

  5. Development of a fluorescence-based sensor for rapid diagnosis of cyanide exposure.

    PubMed

    Jackson, Randy; Oda, Robert P; Bhandari, Raj K; Mahon, Sari B; Brenner, Matthew; Rockwood, Gary A; Logue, Brian A

    2014-02-04

    Although commonly known as a highly toxic chemical, cyanide is also an essential reagent for many industrial processes in areas such as mining, electroplating, and synthetic fiber production. The "heavy" use of cyanide in these industries, along with its necessary transportation, increases the possibility of human exposure. Because the onset of cyanide toxicity is fast, a rapid, sensitive, and accurate method for the diagnosis of cyanide exposure is necessary. Therefore, a field sensor for the diagnosis of cyanide exposure was developed based on the reaction of naphthalene dialdehyde, taurine, and cyanide, yielding a fluorescent β-isoindole. An integrated cyanide capture "apparatus", consisting of sample and cyanide capture chambers, allowed rapid separation of cyanide from blood samples. Rabbit whole blood was added to the sample chamber, acidified, and the HCN gas evolved was actively transferred through a stainless steel channel to the capture chamber containing a basic solution of naphthalene dialdehyde (NDA) and taurine. The overall analysis time (including the addition of the sample) was <3 min, the linear range was 3.13-200 μM, and the limit of detection was 0.78 μM. None of the potential interferents investigated (NaHS, NH4OH, NaSCN, and human serum albumin) produced a signal that could be interpreted as a false positive or a false negative for cyanide exposure. Most importantly, the sensor was 100% accurate in diagnosing cyanide poisoning for acutely exposed rabbits.

  6. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

    PubMed Central

    Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

    2015-01-01

    Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

  7. Wavelet-synchronization methodology: a new approach for EEG-based diagnosis of ADHD.

    PubMed

    Ahmadlou, Mehran; Adeli, Hojjat

    2010-01-01

    A multi-paradigm methodology is presented for electroencephalogram (EEG) based diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) through adroit integration of nonlinear science; wavelets, a signal processing technique; and neural networks, a pattern recognition technique. The selected nonlinear features are generalized synchronizations known as synchronization likelihoods (SL), both among all electrodes and among electrode pairs. The methodology consists of three parts: first detecting the more synchronized loci (group 1) and loci with more discriminative deficit connections (group 2). Using SLs among all electrodes, discriminative SLs in certain sub-bands are extracted. In part two, SLs are computed, not among all electrodes, but between loci of group 1 and loci of group 2 in all sub-bands and the band-limited EEG. This part leads to more accurate detection of deficit connections, and not just deficit areas, but more discriminative SLs in sub-bands with finer resolutions. In part three, a classification technique, radial basis function neural network, is used to distinguish ADHD from normal subjects. The methodology was applied to EEG data obtained from 47 ADHD and 7 control individuals with eyes closed. The Radial Basis Function (RBF) neural network classifier yielded a high accuracy of 95.6% for diagnosis of the ADHD in the feature space discovered in this research with a variance of 0.7%.

  8. Serological assays based on recombinant viral proteins for the diagnosis of arenavirus hemorrhagic fevers.

    PubMed

    Fukushi, Shuetsu; Tani, Hideki; Yoshikawa, Tomoki; Saijo, Masayuki; Morikawa, Shigeru

    2012-10-12

    The family Arenaviridae, genus Arenavirus, consists of two phylogenetically independent groups: Old World (OW) and New World (NW) complexes. The Lassa and Lujo viruses in the OW complex and the Guanarito, Junin, Machupo, Sabia, and Chapare viruses in the NW complex cause viral hemorrhagic fever (VHF) in humans, leading to serious public health concerns. These viruses are also considered potential bioterrorism agents. Therefore, it is of great importance to detect these pathogens rapidly and specifically in order to minimize the risk and scale of arenavirus outbreaks. However, these arenaviruses are classified as BSL-4 pathogens, thus making it difficult to develop diagnostic techniques for these virus infections in institutes without BSL-4 facilities. To overcome these difficulties, antibody detection systems in the form of an enzyme-linked immunosorbent assay (ELISA) and an indirect immunofluorescence assay were developed using recombinant nucleoproteins (rNPs) derived from these viruses. Furthermore, several antigen-detection assays were developed. For example, novel monoclonal antibodies (mAbs) to the rNPs of Lassa and Junin viruses were generated. Sandwich antigen-capture (Ag-capture) ELISAs using these mAbs as capture antibodies were developed and confirmed to be sensitive and specific for detecting the respective arenavirus NPs. These rNP-based assays were proposed to be useful not only for an etiological diagnosis of VHFs, but also for seroepidemiological studies on VHFs. We recently developed arenavirus neutralization assays using vesicular stomatitis virus (VSV)-based pseudotypes bearing arenavirus recombinant glycoproteins. The goal of this article is to review the recent advances in developing laboratory diagnostic assays based on recombinant viral proteins for the diagnosis of VHFs and epidemiological studies on the VHFs caused by arenaviruses.

  9. Vibration sensor-based bearing fault diagnosis using ellipsoid-ARTMAP and differential evolution algorithms.

    PubMed

    Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

    2014-06-16

    Effective fault classification of rolling element bearings provides an