Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

Sensor network design based on system-wide reliability criteria. Part I: Objectives

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks.     

Soft sensor for real-time cement fineness estimation

This paper describes the design and implementation of soft sensors to estimate cement fineness. Soft sensors are mathematical models that use available data to provide real-time information on process variables when the information, for whatever reason, is not available by direct measurement. In this application, soft sensors are used to provide information on process variable normally provided by off-line laboratory tests performed at large time intervals. Cement fineness is one of the crucial parameters that define the quality of produced cement. Providing real-time information on cement fineness using soft sensors can overcome limitations and problems that originate from a lack of information between two laboratory tests. The model inputs were selected from candidate process variables using an information theoretic approach. Models based on multi-layer perceptrons were developed, and their ability to estimate cement fineness of laboratory samples was analyzed. Models that had the best performance, and capacity to adopt changes in the cement grinding circuit were selected to implement soft sensors. Soft sensors were tested using data from a continuous cement production to demonstrate their use in real-time fineness estimation. Their performance was highly satisfactory, and the sensors proved to be capable of providing valuable information on cement grinding circuit performance. After successful off-line tests, soft sensors were implemented and installed in the control room of a cement factory. Results on the site confirm results obtained by tests conducted during soft sensor development.     

Evaluation of the FEH Rainfall-Runoff Method for Catchments in the UK

The Flood Estimation Handbook, which was published in 1999, has substantially replaced the Flood Studies Report for design flood estimation in the UK. Although there are substantial differences between the handbook and the report, the philosophy of the rainfall-runoff method has been retained in the handbook. However, there are differences in the equations that have been used to estimate parameters of the rainfall-runoff method.
This paper provides a comprehensive evaluation of the performance of the Flood Estimation Handbook rainfall-runoff method for flood-frequency estimation, using data from eighty-eight catchments in the UK. The performance is evaluated by reference to the flood quantiles obtained from single-site frequency analysis, and comparison is made with the performance of the Flood Studies Report methodology. In general terms, the handbook over-estimates flood quantiles, with respect to (a) those estimated from observed records and (b) the report's quantiles. The paper explores the causes of these over-estimations and seeks linkages with particular catchment characteristics.     

排上大桥1#墩桩基施工的终孔判断

本文以排上大桥1#墩桩基施工为例,介绍了复杂地质情况下桩基终孔判断的控制方法,为类似工程提供参考经验。     

Recursive parameter estimation of exhaust gas oxygen sensors with input-dependent time delay and linear parameters

Exhaust gas oxygen sensors are widely used for emission control in internal combustion engine systems. Due to their working principle and their positioning, these sensors are subject to input-dependent time delays and input-dependent linear parameters. Consequently, the corresponding time delays and linear parameters can vary fast, i.e. at the same rate as the respective input signals. This paper presents an extension of an existing gradient-based least-squares algorithm and its application to recursively estimate the input-dependent time delays and linear parameters of wide-range oxygen sensors in diesel engines. The extended algorithm is applied in a detailed simulation and experimental study involving real wide-range oxygen sensors that are affected by drift, aging, clogging and manipulation. The input-dependent time delay and linear parameter estimates obtained with the proposed recursive algorithm accurately reproduce the estimates obtained with a numerical offline optimization procedure.     

Fusion of Gaussian mixture models for possible mismatches of sensor model

This paper addresses estimation fusion in the presence of possible mismatches of sensor model. The main concerns of the paper lie in two aspects. One is to improve the filter performance of the single sensor when there are possible mismatches about the sensor model. The other one is to adopt a good fusion scheme to combine local estimates. For these purposes, the measurement process of the local sensor is modeled by multiple models firstly, and the IMM (interacting multiple model) estimator is implemented to produce estimates for individual models. Next, we describe the local estimate by a Gaussian mixture rather than a single Gaussian density in the baseline IMM filter. Such a GMM (Gaussian mixture model) representation of the system state allows us to keep the detailed information about the local tracker, which contributes to the further fusion if treated properly. Finally, the fusion of two Gaussian mixtures is done in the probabilistic framework, and a close-form solution is derived without complex numerical operations. Simulation results demonstrate the effectiveness of the proposed approach.     

Detection and mitigation of actuator attacks on small unmanned aircraft systems

Unmanned aircraft systems (UAS) are susceptible to malicious attacks originated by intelligent adversaries, and the actuators constitute one of the critical attack surfaces. In this paper, the problem of detecting and mitigating attacks on the actuators of a small UAS is addressed. Three possible solutions of differing complexity and effectiveness are proposed to address the problem. The first method involves an active detection strategy, whereby carefully designed excitation signals are superimposed on the control commands to increase the detectability of the attack. In the second method, an unknown input observer is designed, which in addition to detecting the attack also estimates the magnitude of the attack. The third method entails designing an actuator system that makes use of variable frequency pulse-width modulated signals to improve the resilience of the actuator against malicious attacks. The effectiveness of the proposed methods is demonstrated using flight experiments and realistic MATLAB simulations that incorporate exogenous disturbances, such as steady winds, atmospheric turbulence, and measurement noise.     

Elimination of drift-like errors in intelligent cyclic A/D converters

The paper presents a new method of elimination of influence of drift-like errors in so called intelligent cyclic A/D converters, in particular, errors caused by drifts (droops) of voltage at the output of sample-and-hold blocks. The method is based on application of the extended multi-dimensional algorithm, which estimates simultaneously values of the input sample and drift rate. Implementation of the extended algorithm in the intelligent cyclic A/D converters requires only insignificant changes in the digital part of the converter and does not increase their production costs. The motivations to these investigations resulted from practical realizations of the intelligent cyclic A/D converter in CMOS technology and difficulties in design of a precise sample-and-hold circuit. The results of selected simulation experiments related to analysis of influence of a droop rate on the final performance of the intelligent cyclic A/D converters employing the standard (one-dimensional) and extended algorithms are discussed and compared in the paper. The results of experiments show that application of the proposed solution enables efficient functioning of the converters even in the presence of relatively large drifts.