Coronary heart disease optimization system on adaptive-network-based fuzzy inference system and linear discriminant analysis (ANFIS–LDA)

Coronary heart disease is a great concern in the field of healthcare, and one of the main causes of death across the world. In the USA, as in Europe, it is responsible for the highest mortality rate. Although the risk of coronary heart disease has been recognized, few studies have been conducted on this topic. On the other hand, computer science has become an important part of our lives. The use of medicine and medical science-related artificial intelligence facilitating the diagnosis and analysis of diseases and health problems is attracting considerable attention. The present study focuses on the determination of the optimum method for using artificial intelligence in a clinical decision support system in order to provide a solution and diagnosis regarding the research and medical issues related to the application of such a system. In the present study, we have developed a prediction model capable of the risk assessment of coronary heart disease by optimizing an adaptive-network-based fuzzy inference system (ANFIS) and linear discriminant analysis (LDA) on the basis of the dataset of Korean National Health and Nutrition Examinations Survey V. The ANFIS–LDA method, which is optimized using a hybrid method, exhibits a high prediction rate of 80.2 % and is more efficient and effective than the existing methods. We expect that our study to contribute to the prevention of coronary heart disease.     

Languages as hyperplanes: grammatical inference with string kernels

Using string kernels, languages can be represented as hyperplanes in a high dimensional feature space. We discuss the language-theoretic properties of this formalism with particular reference to the implicit feature maps defined by string kernels, considering the expressive power of the formalism, its closure properties and its relationship to other formalisms. We present a new family of grammatical inference algorithms based on this idea. We demonstrate that some mildly context-sensitive languages can be represented in this way and that it is possible to efficiently learn these using kernel PCA. We experimentally demonstrate the effectiveness of this approach on some standard examples of context-sensitive languages using small synthetic data sets.     

Operational definition and automated inference of test-driven development with Zorro

Test-driven development (TDD) is a style of development named for its most visible characteristic: the design and implementation of test cases prior to the implementation of the code required to make them pass. Many claims have been made for TDD: that it can improve implementation as well as design quality, that it can improve productivity, that it results in 100% coverage, and so forth. However, research to validate these claims has yielded mixed and sometimes contradictory results. We believe that at least part of the reason for these results stems from differing interpretations of the TDD development style, along with an inability to determine whether programmers actually follow whatever definition of TDD is in use. Zorro is a system designed to automatically determine whether a developer is complying with an operational definition of Test-Driven Development (TDD) practices. Automated recognition of TDD can benefit the software development community in a variety of ways, from inquiry into the “true nature” of TDD, to pedagogical aids to support the practice of test-driven development, to support for more rigorous empirical studies on the effectiveness of TDD in both laboratory and real world settings.     

The mask of (2b + 4)-point n-ary subdivision scheme

In this paper, we present general formulae for the mask of (2b + 4)-point n-ary approximating as well as interpolating subdivision schemes for any integers ${b\,\geqslant\,0}$ and ${n\,\geqslant\,2}$ . These formulae corresponding to the mask not only generalize and unify several well-known schemes but also provide the mask of higher arity schemes. Moreover, the 4-point and 6-point a-ary schemes introduced by Lian [Appl Appl Math Int J 3(1):18–29, 2008] are special cases of our general formulae.     

Creating MPU implicit surfaces from unoriented point sets with orientation inference

In this paper, we extend the MPU implicits algorithm to deal with unoriented point sets while preserving its desirable properties, such as sharp feature preservation. An orientation inference algorithm is introduced to orientate the local implicit patches by solving a graph labeling problem through energy minimization. Sign consistency between local functions is exploited to infer the globally consistent orientation. To precisely model the features, we employ the affinity propagation clustering algorithm to identify the local surface patches composing the features by considering orientation consistency between data points. Sharp features can then be accurately reconstructed by performing piecewise smooth surface fitting. Experimental results are shown to demonstrate the performance of the proposed algorithm.     

A calibration approach based on Takagi–Sugeno fuzzy inference system for digital electronic compasses

Recently, the development of industrial processes brought on the outbreak of technologically complex systems. This development generated the necessity of research relative to the mathematical techniques that have the capacity to deal with project complexities and validation. Fuzzy models have been receiving particular attention in the area of nonlinear systems identification and analysis due to it is capacity to approximate nonlinear behavior and deal with uncertainty. A fuzzy rule-based model suitable for the approximation of many systems and functions is the Takagi–Sugeno (TS) fuzzy model. TS fuzzy models are nonlinear systems described by a set of if then rules which gives local linear representations of an underlying system. Such models can approximate a wide class of nonlinear systems. In this paper a performance analysis of a system based on TS fuzzy inference system for the calibration of electronic compass devices is considered. The contribution of the evaluated TS fuzzy inference system is to reduce the error obtained in data acquisition from a digital electronic compass. For the reliable operation of the TS fuzzy inference system, adequate error measurements must be taken. The error noise must be filtered before the application of the TS fuzzy inference system. The proposed method demonstrated an effectiveness of 57% at reducing the total error based on considered tests.     

A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters

An important issue in application of fuzzy inference systems (FISs) to a class of system identification problems such as prediction of wave parameters is to extract the structure and type of fuzzy if–then rules from an available input–output data set. In this paper, a hybrid genetic algorithm–adaptive network-based FIS (GA–ANFIS) model has been developed in which both clustering and rule base parameters are simultaneously optimized using GAs and artificial neural nets (ANNs). The parameters of a subtractive clustering method, by which the number and structure of fuzzy rules are controlled, are optimized by GAs within which ANFIS is called for tuning the parameters of rule base generated by GAs. The model has been applied in the prediction of wave parameters, i.e. wave significant height and peak spectral period, in a duration-limited condition in Lake Michigan. The data set of year 2001 has been used as training set and that of year 2004 as testing data. The results obtained by the proposed model are presented and analyzed. Results indicate that GA–ANFIS model is superior to ANFIS and Shore Protection Manual (SPM) methods in terms of their prediction accuracy.     

Adaptive neuro-fuzzy inference system–based model for elevation–surface area–storage interrelationships

In the developing of an optimal operation schedule for dams and reservoirs, reservoir simulation is one of the critical steps that must be taken into consideration. For reservoirs to have more reliable and flexible optimization models, their simulations must be very accurate. However, a major problem with this simulation is the phenomenon of nonlinearity relationships that exist between some parameters of the reservoir. Some of the conventional methods use a linear approach in solving such problems thereby obtaining not very accurate simulation most especially at extreme values, and this greatly influences the efficiency of the model. One method that has been identified as a possible replacement for ANN and other common regression models currently in use for most analysis involving nonlinear cases in hydrology and water resources–related problems is the adaptive neuro-fuzzy inference system (ANFIS). The use of this method and two other different approaches of the ANN method, namely feedforward back-propagation neural network and radial basis function neural network, were adopted in the current study for the simulation of the relationships that exist between elevation, surface area and storage capacity at Langat reservoir system, Malaysia. Also, another model, auto regression (AR), was developed to compare the analysis of the proposed ANFIS and ANN models. The major revelation from this study is that the use of the proposed ANFIS model would ensure a more accurate simulation than the ANN and the classical AR models. The results obtained showed that the simulations obtained through ANFIS were actually more accurate than those of ANN and AR; it is thus concluded that the use of ANFIS method for simulation of reservoir behavior will give better predictions than the use of any new or existing regression models.     

The GPSS—GASP combined (GGC) system

Each one of the popular discrete event simulation languages GPSS and GASP IV has a large user community. GPSS is probably the most widely used discrete event simulation language; it is appreciated for its ease of use and for the structural similarity of a GPSS model to the system it represents. GASP IV has provided (along with its successor, SLAM) a viable, well-documented system for combined continuous/discrete modeling and simulation. This paper presents the design and prototype development of a simulation language system called GGC, based upon a combination of GPSS and GASP IV. The new language system seeks to provide the “best of both worlds” in supporting the use of a GASP-based continuous mode in conjunction with GPSS use for discrete event modeling. An example of GGC use in a combined continuous/discrete model is presented. Possible future approaches are suggested based on conclusions drawn.     

Adaptive behaviors of reactive mobile robot with Bayesian inference in nonstationary environments

This paper presents a technique for a reactive mobile robot to adaptively behave in unforeseen and dynamic circumstances. A robot in nonstationary environments needs to infer how to adaptively behave to the changing environment. Behavior-based approach manages the interactions between the robot and its environment for generating behaviors, but in spite of its strengths of fast response, it has not been applied much to more complex problems for high-level behaviors. For that reason many researchers employ a behavior-based deliberative architecture. This paper proposes a 2-layer control architecture for generating adaptive behaviors to perceive and avoid moving obstacles as well as stationary obstacles. The first layer is to generate reflexive and autonomous behaviors with behavior network, and the second layer is to infer dynamic situations of the mobile robot with Bayesian network. These two levels facilitate a tight integration between high-level inference and low-level behaviors. Experimental results with various simulations and a real robot have shown that the robot reaches the goal points while avoiding stationary or moving obstacles with the proposed architecture.     

A perishable inventory system with modified (S−1,S) policy and arbitrary processing times

In this article we analyze a lost sales (S−1,S) perishable system, under Poisson demands and exponential lifetimes, in which the reorders are placed at every demand epoch so as to take the inventory position back to its maximum level S. The items are replenished one at a time and the resupply time has arbitrary distribution. The various operating characteristics are obtained using Markov renewal techniques. A matrix recursive scheme is developed to determine the stationary distribution of the underlying Markov chain. The efficiency of this procedure in the determination of optimal S that minimizes the long run expected cost rate is discussed. Sensitivity analysis of various system parameters is also carried out.Scope and purposeThe analysis of perishable inventory systems, which has potential applications in various sectors of industry, is far more difficult than their infinite lifetime counterparts. In the study of perishable systems under traditional (S−1,S) policy, reorders for items are placed at demand as well as failure epochs. However, it will be more meaningful not to place orders at failure epochs, which is also practical and economical since it avoids continuous monitoring of items. This paper deals with a continuous review perishable system under a more realistic base stock policy, with variable ordering quantity and arbitrary unit resupply times. The operating characteristics of this complex model are derived using the techniques of semi-regenerative processes. A matrix recursive scheme developed to determine the stationary distribution has enabled us to code an efficient numerical program for cost optimization.     

An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy

Manufacturing enterprises are collaborating among each other in manufacturing service ecosystems (MSE) with the objective to compose and provision numerous product–services (P–S) on the market. However, many paramount processes outset much before the actual composition, like the strategy planning of those P–S. Such decisions are usually full of ambiguities with complex sets of decisional possibilities, which are extremely hard to encompass even within a decision support system. Thus, the aim of this article is to undergird the development of an effective decision support system (DSS) for solving the challenge of planning a P–S strategy within a MSE, as well to present and apply a relative novel fuzzy inference technique, in order to build the DSS in question. This is achieved by first designing the logical data model that conceptualizes the context of planning a P–S strategy within a MSE, secondly by designing the actual business intelligence (BI) sets of rules and thirdly to build a DSS and test its data. As the input data needed to plan a strategy are often intangible, without a clear delineation among classes (e.g. “Market_1 is more competitive than Market_2”), with more than just binary values that can also overlap among each other and can be expressed using human language, a fuzzy based inference system is used to build the BI rules set. The DSS provides answers to three central uncertainties in P–S strategy planning expressed in the article as performance questions.     

System reliability for non-repairable multi-state series–parallel system using fuzzy Bayesian inference based on prior interval probabilities

In this article, the fuzzy concepts are applied in analysis of the system reliability problem. The fuzzy number is used to construct the fuzzy reliability of the non-repairable multi-state series–parallel system (NMSS). The fuzzy failure rate function is represented by an exponential fuzzy number. By using this innovative approach, the fuzzy system reliability of NMSS is created. In order to analyse this fuzzy system reliability, the fuzzy Bayesian point estimate of fuzzy system reliability is made by the conventional Bayesian formula. And, the posterior fuzzy system reliability of NMSS is developed by Bayesian inference with fuzzy probabilities. Finally, the performance of the method is measured by the mean square error of fuzzy Bayesian point estimate for the fuzzy system reliability of NMSS.     

A multi-echelon production–inventory system with supply disruption

The article investigates an integrated multi-layer supply chain model consisting of supplier, manufacturer and retailer while supply disruption, machine breakdown, safety stock, maintenance breakdown occur simultaneously. At beginning of the production, manufacturer keeps some raw materials in stock received from second supplier at high price, as safety stock due to supply disruption of first supplier. Corrective maintenance is done immediately to restore its normal stage when machine breakdown occurs. Stock out situations at manufacturer and retailer are considered due to disruption of production for machine breakdown. The integrated expected costs of the chain in centralized (collaborating) and decentralized (Stakelberg approach) system are compared. A numerical example and its sensitivity analysis are provided to test feasibility of the model.     

Integrating RFID with quality assurance system – Framework and applications

A quality assurance system (QAS) is designed and used to inspect product quality, determine causes of abnormal by collecting, analyzing and testing data from the product line, and then determining how an improvement plan should be conducted. By utilizing radio frequency identification (RFID), a QAS may be able to detect, and even prevent, quality problems more effectively than the traditional quality assurance system. The proposed framework provides a structure for an RFID-based QAS that allows on-site staff to monitor complicated variations in production process by handling numerous possible abnormalities simultaneously. Two industry applications are provided to demonstrate using of the proposed framework.     

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model （GFHM）. Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.     

Erratum to “A stochastic controller for a scalar linear system with additive Cauchy noise” [Automatica 50(1) (2014) 114–127]

A stochastic control scheme is developed for scalar, discrete-time, and linear-dynamic systems driven by Cauchy distributed process and measurement noises. When addressing the optimal control problem for such systems, the standard quadratic cost criteria cannot be used. In this study we introduce a new objective function that is functionally similar to the Cauchy probability density function. The performance index, defined as the expectation of this objective function with respect to the Cauchy densities, exists. The dynamic programming solution to the fixed and finite horizon optimal control problem that uses this performance index appears to be intractable. Therefore, a moving horizon optimal model predictive control problem is implemented, for which the conditional expected value of the objective function and its gradients can be computed in closed   form and without assumptions such as certainty equivalence. Numerical results are shown for this m$m$-step model predictive optimal controller and compared to a similar, Linear–Exponential–Gaussian model predictive controller. An essential difference between the Cauchy and Gaussian controllers when applied to a system with Cauchy noises is that, while the Gaussian controller is linear and reacts strongly to all noise pulses, the Cauchy controller can differentiate between measurement and process noise pulses by ignoring the former while responding to the latter. This property of the Cauchy controller occurs when an impulsive measurement noise is more likely than an impulsive process noise. The Cauchy and Gaussian controllers react similarly when applied to a system with Gaussian noises, demonstrating the robustness of the proposed control scheme.     

Coupled solid (FVM)–fluid (DSMC) simulation of micro-nozzle with unstructured-grid

The flow field and temperature distributions of free molecular micro-electro-thermal resist jet (FMMR) were studied resorting to DSMC–FVM coupled method. Direct simulation Monte Carlo (DSMC) method is the most useful tool to simulate the flow field of FMMR and unstructured grid is suitable for the flow simulation in a complicated region with tilted wall surface. DSMC code based on unstructured grid system was developed and the result was compared with that of structured grid and analytical solution to validate the reliability of the developed code. The DSMC method was then used to simulate the fluid flow in the micro-nozzle (Kn > 0.01) and the temperature distribution in the nozzle wall was obtained by the finite volume method (FVM). The Dirichlet–Neumann method was used to couple the wall heat flux and temperature between flow field and solid area. The effect of different income pressure was studied in detail and the results showed that the temperature of solid area changed drastically at different income pressure, so the commonly-adopted method of pre-setting boundary temperature before simulation was unreasonable.     

MLMATERN: A computer program for maximum likelihood inference with the spatial Matérn covariance model

The Matérn covariance scheme is of great importance in many geostatistical applications where the smoothness or differentiability of the random field that models a natural phenomenon is of interest. In addition to the range and nugget parameters, the flexibility of the Matérn model is provided by the so-called smoothness parameter which controls the degree of smoothness of the random field. It has been the usual practice in geostatistics to fit theoretical semivariograms like the spherical or exponential, thus implicitly assuming the smoothness parameter to be known, without questioning if there is any theoretical or empirical basis to justify such assumption. On the other hand, if only a small number of sparse experimental data are available, it is more critical to ask if the smoothness parameter can be identified with statistical reliability. Maximum likelihood estimation of spatial covariance parameters of the Matérn model has been used to address the previous questions. We have developed a general algorithm for estimating the parameters of a Matérn covariance (or semivariogram) scheme, where the model may be isotropic or anisotropic, the nugget variance can be included in the model if desired, and the uncertainty of the estimates is provided in terms of variance–covariance matrix (or standard error-coefficient of correlation matrix) as well as likelihood profiles for each parameter in the covariance model. It is assumed that the empirical data are a realization of a Gaussian process. Our program allows the presence of a polynomial trend of order zero (constant global mean), one (linear trend) or two (quadratic trend). The restricted maximum likelihood method has also been implemented in the program as an alternative to the standard maximum likelihood. Simulation results are given in order to investigate the sampling distribution of the parameters for small samples. Furthermore, a case study is provided to show a real practical example where the smoothness parameter needs to be estimated.