An adaptive human–machine control system based on multiple fuzzy predictive models of operator functional state

Under the framework of adaptive Human–Machine (HM) systems, it has been proposed that human operators’ task level should be dynamically adjusted according to his/her functional state. The construction of models that can reliably predict the operator functional state (OFS) becomes critical to accomplish such adjustments. However, most of the existing models that evaluate the current OFS by using operators’ current physiological data are static and are of no real predictive capability. Thus, when they are used in adaptive HM systems, the resultant task allocation between operators and machines would be time-delayed. To overcome this problem, a one-step-ahead predictive model concept for OFS computation is proposed. Meanwhile, multiple fuzzy models are developed by using the Wang–Mendel method. These models are able to increase the accuracy of the OFS breakdown prediction, as well as to reduce the model training time. In addition, an adaptive task allocation strategy is designed to validate the proposed models. The results demonstrate that, compared to the conventional HM systems, a 6.7% OFS increment and a 57.1% OFS breakdown decrement can be obtained in the multiple models based adaptive HM systems. The multiple predictive models and the adaptive task allocation strategy would pave the way for future implementations of real-time adaptive HM systems.     

Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method

The human operator’s ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human–automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety–critical human–machine cooperative systems.     

Assessment of human operator functional state using a novel differential evolution optimization based adaptive fuzzy model

With the development of human–machine systems, there has been a growing concern about the consequences of operator performance breakdown under excessive level of workload, especially in safety-critical situations. Assessment and detection of the operator functional state (OFS) enable us to predict the high operational risks of operator. This paper adopts the psychophysiological signals and task performance measures to evaluate OFS under different levels of mental workload. Four indices extracted from electrocardiogram and electroencephalogram, including heart rate (HR), ratio of the standard deviation to the average of HR segment, task load indices (TLI1 and TLI2), are chosen as the inputs of the proposed model. A technique of differential evolution with ant colony search (DEACS) is developed to optimize the parameters of Adaptive-Network-based Fuzzy Inference System (ANFIS). The optimized ANFIS model is employed to estimate the OFS under a series of process control tasks on a simulated software platform of AUTOmation-enhanced Cabin Air Management System. The results showed that the proposed adaptive fuzzy model based on ANFIS and DEACS algorithm is applicable for the operator functional state assessment.     

Oligofructose Promotes Satiety in Rats Fed a High‐Fat Diet: Involvement of Glucagon‐Like Peptide‐1

Objective: To analyze the putative interest of oligofructose (OFS) in the modulation of food intake after high‐fat diet in rats and to question the relevance of the expression and secretion of intestinal peptides in that context. Research Methods and Procedures: Male Wistar rats were pretreated with standard diet or OFS‐enriched (10%) standard diet for 35 days followed by 15 days of high‐fat diet enriched or not with OFS (10%) treatment. Body weight, food intake, triglycerides, and plasma ghrelin levels were monitored during the treatment. On day 50, rats were food‐deprived 8 hours and anesthetized for blood and intestinal tissue sampling for further proglucagon mRNA, glucagon‐like peptide (GLP)‐1, and GLP‐2 quantification. Results: The addition of OFS in the diet protects against the promotion of energy intake, body weight gain, fat mass development, and serum triglyceride accumulation induced by a high‐fat diet. OFS fermentation leads to an increase in proglucagon mRNA in the cecum and the colon and in GLP‐1 and GLP‐2 contents in the proximal colon, with consequences on the portal concentration of GLP‐1 (increase). A lower ghrelin level is observed only when OFS is added to the standard diet of rats. Discussion: In rats exposed to high‐fat diet, OFS is, thus, able to modulate endogenous production of gut peptides involved in appetite and body weight regulation. Because several approaches are currently used to treat type 2 diabetes and obesity with limited effectiveness, dietary fibers such as OFS, which promote the endogenous production of gut peptides like GLP‐1, could be proposed as interesting nutrients to consider in the management of fat intake and associated metabolic disorders.     

Regression Calibration in Semiparametric Accelerated Failure Time Models

Summary In large cohort studies, it often happens that some covariates are expensive to measure and hence only measured on a validation set. On the other hand, relatively cheap but error‐prone measurements of the covariates are available for all subjects. Regression calibration (RC) estimation method ( Prentice, 1982 , Biometrika 69 , 331–342) is a popular method for analyzing such data and has been applied to the Cox model by Wang et al. (1997, Biometrics 53 , 131–145) under normal measurement error and rare disease assumptions. In this article, we consider the RC estimation method for the semiparametric accelerated failure time model with covariates subject to measurement error. Asymptotic properties of the proposed method are investigated under a two‐phase sampling scheme for validation data that are selected via stratified random sampling, resulting in neither independent nor identically distributed observations. We show that the estimates converge to some well‐defined parameters. In particular, unbiased estimation is feasible under additive normal measurement error models for normal covariates and under Berkson error models. The proposed method performs well in finite‐sample simulation studies. We also apply the proposed method to a depression mortality study.     

Hidden Markov models incorporating fuzzy measures and integrals for protein sequence identification and alignment

Profile hidden Markov models （HMMs） based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein sequences belonging to a given family. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures and Choquet integrals, thus further extends the generalized HMM. Based on the fuzzified forward and backward variables, we propose a fuzzy Baum-Welch parameter estimation algorithm for profiles. The strong correlations and the sequence preference involved in the protein structures make this fuzzy architecture based model as a suitable candidate for building profiles of a given family, since the fuzzy set can handle uncertainties better than classical methods.     

Two-dimensional grey cloud clustering-fuzzy entropy comprehensive assessment model for river health evaluation

Abstract

The river health evaluation is typically complex non-linear system with characteristics of fuzziness and randomness. However, conventional gray clustering method has difficult to effectively describe fuzzy and random information simultaneously. For this purpose, the cloud model and fuzzy entropy theory are introduced to establish 2D gray cloud clustering-fuzzy entropy comprehensive evaluation model. Different with health level models, it reflects river health situation from aspects of health level and corresponding water body complexity simultaneously. The health level is obtained by gray cloud whitened weight function (first sub-system) and fuzzy entropy represents complexity and fuzziness of river health situation (second sub-system). Moreover, multi-level river health evaluation indicator system is constructed with dividing indicators into common and distinct sections according to differences on river characteristics. Meanwhile, indicator weights are determined by renewed combined weighting method based on minimum deviation principle. Finally, we conduct health evaluation work for rivers in the Taihu basin. The evaluation health levels and fuzzy entropy for river A–G are H3 (0.4888, relatively significant); H2 (0.5476, relatively fuzzy); H2 (0.7526, fuzzy); H2 (0.4731, relatively significant); H2 (05138, relatively fuzzy); H3 (0.5822, relatively fuzzy), and H2 (0.4064, relatively significant), respectively. Results are consistent with current river health situation and more intuitive than compared models. Furthermore, evaluation results with four different weighting methods are compared to further demonstrate rationality of the weighting method and evaluation model. Hence, the model proposed is demonstrated to provide new insight for solving river health assessment problem effectively.     

Assessment of methods to estimate aquatic macrophyte species richness in extrapolated sample sizes

We evaluated six methods to estimate species richness in extrapolated sample size using presence–absence data for aquatic macrophyte assemblages. Methods suitable for assemblages involving terrestrial and non-clonal (unitary) organisms may not be valid for aquatic macrophytes. The extrapolation of a species accumulation curve using a logarithmic function or using a linear model on the log of accumulated sampling units consistently overestimated species richness. The newly proposed Total-Species method gave similar results. The Negative Binomial and Logarithmic Series methods and the recently proposed Binomial Mixture Model were unbiased and accurate. We conclude that current extrapolation techniques are valid for estimation of species richness in macrophyte assemblages, and recommend the Logarithmic Series, Binomial Negative or Binomial Mixture Model methods.     

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).     

Smoothed quantile regression analysis of competing risks

Censored quantile regression models, which offer great flexibility in assessing covariate effects on event times, have attracted considerable research interest. In this study, we consider flexible estimation and inference procedures for competing risks quantile regression, which not only provides meaningful interpretations by using cumulative incidence quantiles but also extends the conventional accelerated failure time model by relaxing some of the stringent model assumptions, such as global linearity and unconditional independence. Current method for censored quantile regressions often involves the minimization of the L₁‐type convex function or solving the nonsmoothed estimating equations. This approach could lead to multiple roots in practical settings, particularly with multiple covariates. Moreover, variance estimation involves an unknown error distribution and most methods rely on computationally intensive resampling techniques such as bootstrapping. We consider the induced smoothing procedure for censored quantile regressions to the competing risks setting. The proposed procedure permits the fast and accurate computation of quantile regression parameter estimates and standard variances by using conventional numerical methods such as the Newton–Raphson algorithm. Numerical studies show that the proposed estimators perform well and the resulting inference is reliable in practical settings. The method is finally applied to data from a soft tissue sarcoma study.     

The train fueling cost minimization problem with fuzzy fuel prices

The train fueling cost minimization problem is to find a scheduling and fueling strategy such that the fueling cost is minimized and no train runs out of fuel. Since fuel prices vary by location and time from month to month, we estimate them by fuzzy variables in this paper. Furthermore, we propose a fuzzy fueling cost minimization model by minimizing the expected fueling cost under the traversing time constraint, maximal allowable speed constraint, tank capacity constraint, and so on. In order to solve the model, we decompose it into a nonlinear scheduling strategy model and a linear fueling strategy model. Based on the Karush–Kuhn–Tucker conditions, we design an iterative algorithm to solve the scheduling strategy model, and furthermore design a numerical algorithm to solve the fuzzy fueling cost minimization model. Finally, some numerical examples are presented for showing the efficiency of the proposed approach on saving fueling cost.     

Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants

A new approach to nonlinear modeling and adaptive monitoring using fuzzy principal component regression (FPCR) is proposed and then applied to a real wastewater treatment plant (WWTP) data set. First, principal component analysis (PCA) is used to reduce the dimensionality of data and to remove collinearity. Second, the adaptive credibilistic fuzzy-c-means method is used to appropriately monitor diverse operating conditions based on the PCA score values. Then a new adaptive discrimination monitoring method is proposed to distinguish between a large process change and a simple fault. Third, a FPCR method is proposed, where the Takagi-Sugeno-Kang (TSK) fuzzy model is employed to model the relation between the PCA score values and the target output to avoid the over-fitting problem with original variables. Here, the rule bases, the centers and the widths of TSK fuzzy model are found by heuristic methods. The proposed FPCR method is applied to predict the output variable, the reduction of chemical oxygen demand in the full-scale WWTP. The result shows that it has the ability to model the nonlinear process and multiple operating conditions and is able to identify various operating regions and discriminate between a sustained fault and a simple fault (or abnormalities) occurring within the process data.     

农药残留预测模型参数估计过程的灵敏度分析

本文根据矩阵理论运用摄动分析的方法,揭示了最小二乘法参数估计过程“病态”正规方程组问题,及运用“病态”正规方程组进行参数估计给预测模型带来的影响．最后根据矩阵的条件数理论,提出了对农药残留预测模型参数估计过程进行灵敏度分析的数学方法．     

On‐line yeast propagation process monitoring and control using an intelligent automatic control system

The monitoring and control of bioprocesses is a challenging task. This applies particularly if the actions to the process have to be carried out in real‐time. This work presents a system for on‐line monitoring and control of batch yeast propagation under limiting conditions based on a virtual plant operator, which uses the concept of intelligent control algorithms by means of fuzzy logic theory. Process information is provided on‐line using a sensor array comprising the measurement of OD, operating temperature, pressure, density, dissolved oxygen, and pH value. In this context practical problems arising through on‐line sensing and signal processing are addressed. The preprocessed sensor data are fed to a neural network for on‐line biomass estimation. The root mean squared error of prediction is 4 × 10⁶ cells/mL. The proposed system then triggers temperature and aeration by usage of a temperature dependent metabolic growth model and sensor data. The deviation of the predicted biomass from that of the reference trajectory as modeled by the metabolic growth model and its temporal derivative are used as inputs for the fuzzy temperature controller. The inputs used by the fuzzy aeration controller are the deviation of measured extract from that of the reference trajectory, the predicted cell count, and the dissolved oxygen concentration. The fuzzy‐based expert system allows to provide the desired yeast cell concentration of 100–120 × 10⁶ cells/mL at a minimum residual extract limit of 6.0 g/100 g at the required point of time. Thus, a dynamic adjustment of the propagation process to the overall production schedule is possible in order to produce the required amount of biomass at the right time.     

On parameter estimation for biaxial mechanical behavior of arteries

This article considers the parameter estimation of multi-fiber family models for biaxial mechanical behavior of passive arteries in the presence of the measurement errors. First, the uncertainty propagation due to the errors in variables has been carefully characterized using the constitutive model. Then, the parameter estimation of the artery model has been formulated into nonlinear least squares optimization with an appropriately chosen weight from the uncertainty model. The proposed technique is evaluated using multiple sets of synthesized data with fictitious measurement noises. The results of the estimation are compared with those of the conventional nonlinear least squares optimization without a proper weight factor. The proposed method significantly improves the quality of parameter estimation as the amplitude of the errors in variables becomes larger. We also investigate model selection criteria to decide the optimal number of fiber families in the multi-fiber family model with respect to the experimental data balancing between variance and bias errors.     

Variable selection in nonlinear modeling based on RBF networks and evolutionary computation

In this paper a novel variable selection method based on Radial Basis Function (RBF) neural networks and genetic algorithms is presented. The fuzzy means algorithm is utilized as the training method for the RBF networks, due to its inherent speed, the deterministic approach of selecting the hidden node centers and the fact that it involves only a single tuning parameter. The trade-off between the accuracy and parsimony of the produced model is handled by using Final Prediction Error criterion, based on the RBF training and validation errors, as a fitness function of the proposed genetic algorithm. The tuning parameter required by the fuzzy means algorithm is treated as a free variable by the genetic algorithm. The proposed method was tested in benchmark data sets stemming from the scientific communities of time-series prediction and medicinal chemistry and produced promising results.     

Domain position prediction based on sequence information by using fuzzy mean operator

The prediction of protein domain region is an advantageous process on the study of protein structure and function. In this study, we proposed a new method, which is composed of fuzzy mean operator and region division, to predict the particular positions of domains in a target protein based on its sequence. The whole sequence is aligned and scored by using fuzzy mean operator, and the final determination of domain region position is realized by region division. A published benchmark is used for the comparison with previous researches. In addition, we generate two extra datasets to examine the stability of this method. Finally, the prediction accuracy of independent test dataset achieved by our method was up to 84.13%. We wish that this method could be useful for related researches. Proteins 2015; 83:1462–1469. © 2015 Wiley Periodicals, Inc.     

Spline-based semiparametric projected generalized estimating equation method for panel count data

We propose to analyze panel count data using a spline-based semiparametric projected generalized estimating equation (GEE) method with the proportional mean model E(N(t)|Z) = Λ(0)(t) e(β(0)(T)Z). The natural logarithm of the baseline mean function, logΛ(0)(t), is approximated by a monotone cubic B-spline function. The estimates of regression parameters and spline coefficients are obtained by projecting the GEE estimates into the feasible domain using a weighted isotonic regression (IR). The proposed method avoids assuming any parametric structure of the baseline mean function or any stochastic model for the underlying counting process. Selection of the working covariance matrix that accounts for overdispersion improves the estimation efficiency and leads to less biased variance estimations. Simulation studies are conducted using different working covariance matrices in the GEE to investigate finite sample performance of the proposed method, to compare the estimation efficiency, and to explore the performance of different variance estimates in presence of overdispersion. Finally, the proposed method is applied to a real data set from a bladder tumor clinical trial.     

基于重采样技术的肌电信号动作电位传导速度估计研究

目的：本文针对表面肌电（sEMG）信号探讨动作电位传导速度（APCV）估计问题。方法：以生理学仿真sEMG信号为基础,采用基于互相关分析的时延估计技术来获取相应的APCV估计值,并利用重采样技术来提高估计的精度。结果：实验表明。针对重采样后的仿真信号,其APCV的估计误差得到了明显降低。结论：所采用方法能够有效获取满意的APCV估计效果。