A novel Bayesian deep learning methodology for enhanced foetal cardiac signal mining

Cardiac defects are amongst the most common birth defects. Cardiac diagnosis is indispensably imperative in the foetal stage as it might help provide an opportunity to plan and manage the baby during Antepartum and Intrapartum stages, when the baby is born. It is from the Antepartum stage where the foetal electrocardiogram (fECG) signal can actually be detected. At present, monitoring the foetus is completely focused on the heart rate. Currently fECG analysis is used in the clinical domain to analyse heart rate and the allied variations. Analysis using the morphology of the fECG is generally not undertaken for cardiac-anomaly populations. The ultimate reason for this scenario is due to unavailability in technology to yield trustworthy fECG measurements with desired quality required by Physicians. A novel hybrid methodology called BDL (Bayesian Deep Learning) methodology is proposed. The BDL includes a Bayesian filter and a deep learning (DL) Artificial Intelligent neural network for maternal electrocardiogram (mECG) elimination and non-linear artefacts removal to yield high quality non-invasive fECG signal. The outcomes of the research by the proposed BDL system proved valuable and provided high quality fECG signal for efficient foetal diagnosis.     

Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems

Abstract: A new approach based on an adaptive neuro‐fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records. The ANFIS classifier is used to detect diabetes diseases when eight features defining diabetes indications are used as inputs. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. The conclusions concerning the impacts of features on the diagnosis of diabetes disease are obtained through analysis of the ANFIS. The performance of the ANFIS model is evaluated in terms of training performances and classification accuracies and the results confirm that the proposed ANFIS model has potential in detecting diabetes diseases.     

Ontological fuzzy agent for electrocardiogram application

The electrocardiogram (ECG) signal is adopted extensively as a low-cost diagnostic procedure to provide information concerning the healthy status of the heart. However, the QRS complex must be calculated accurately before proceeding with the heart rate variability (HRV). In particular, the R peak needs to be detected reliably. This study presents an adaptive fuzzy detector to detect the R peak correctly. Additionally, an ontological fuzzy agent is presented to process the collection of ECG signals. The required knowledge is stored in the ontology, which comprises some personal ontologies and predefined by domain experts. The ontological fuzzy agent retrieves the ECG signals with R peaks marked for HRV analysis and ECG further applications. It contains a personal fuzzy filter, an HRV analysis mechanism, and a fuzzy normed inference engine. Moreover, the ECG fuzzy signal space and some important properties are presented to define the working environment of the agent. An experimental platform has been constructed to test the performance of the agent. The results indicate that the proposed method can work effectively.     

强杂波背景下LPI信号的模糊检测方法

研究了强杂波干扰背景下运用模糊集合理论解决低截获概率信号（LPI）雷达信号的检测问题,分析了在无源雷达体制下获取有效目标信号的方法,并指出了传统匹配滤波方法的局限性.针对该问题,给出了四种模糊集合相似性测度,在借鉴了传统的匹配滤波器基础上提出了构造模糊匹配滤波器,并利用模糊相似性测度为准则进行滤波运算,以解决强干扰背景中信号检测的问题.基于定义的相似性测度准则对LPI信号采用模糊匹配滤波,仿真结果表明该方法具有在强干扰背景下检测目标的良好能力,其性能优于传统匹配滤波方法.     

Neuro‐Fuzzy Impact Control (NFIC) for Anti‐Personnel (AP) Mines Detection

An explicit impact control scheme is modified as the main control scheme, while an intelligent control method is designed to deal with uncertainties and varying environment parameters in a mechatronics approach to anti‐personnel (AP) mine detection. The device imitates the manual hand‐prodding technique for mine detection. It inserts a bayonet into the soil and models the dynamics of the manipulator and environment parameters, such as stiffness variation in the soil, to control the impact caused by making contact with a stiff object. An adaptive neuro‐fuzzy plus PID controller is employed to switch from a conventional PID controller to neuro‐fuzzy impact control (NFIC) when an impact is detected. The developed control schemes are validated through experimental work.     

心电信号去噪的数学形态学滤波器

为探索验证一种基于数学形态滤波器的去除心电基线漂移和工频干扰的高性能滤波器设计方法,借鉴数学形态学一维信号滤波原理,提出自适应阈值ECG去噪算法的思路,讨论了3σ统计准则在ECG自适应阈值滤波中的作用,利用改进的算法对心电图中常见的工频干扰和基线漂移进行校正。通过对MIT-BIH心率变异数据库中多组数据的仿真验证研究,验证了该算法能有效实现心电信号的噪声预处理;数学形态学理论在心电信号处理中具有良好性能,是实时处理一维生物医学信号有潜力的工具。     

An evolutionary‐based adaptive neuro‐fuzzy inference system for intelligent short‐term load forecasting

The continuing growth in size and complexity of electric power systems requires the development of applicable load forecasting models to estimate the future electrical energy demands accurately. This paper presents a novel load forecasting approach called genetic‐based adaptive neuro‐fuzzy inference system (GBANFIS) to construct short‐term load forecasting expert systems and controllers. At the first stage, all records of data are searched by a novel genetic algorithm (GA) to find the most suitable feature of inputs to construct the model. Then, determined inputs are fed into the adaptive neuro‐fuzzy inference system to evolve the initial knowledge‐base of the expert system. Finally, the initial knowledge‐base is searched by another robust GA to induce a better cooperation among the rules by rule weight derivation and rule selection mechanisms. We show the superiority and applicability of our approach by applying it to the Iranian monthly electrical energy demand problem and comparing it with the most frequently adopted approaches in this field. Results indicate that GBANFIS outperforms its rival approaches and is a promising tool for dealing with short‐term load forecasting problems.     

Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions

This paper presents an adaptive fuzzy iterative learning control (ILC) design for non-parametrized nonlinear discrete-time systems with unknown input dead zones and control directions. In the proposed adaptive fuzzy ILC algorithm, a fuzzy logic system (FLS) is used to approximate the desired control signal, and an additional adaptive mechanism is designed to compensate for the unknown input dead zone. In dealing with the unknown control direction of the nonlinear discrete-time system, a discrete Nussbaum gain technique is exploited along the iteration axis and applied to the adaptive fuzzy ILC algorithm. As a result, it is proved that the proposed adaptive fuzzy ILC scheme can drive the ILC tracking errors beyond the initial time instants into a tunable residual set as iteration number goes to infinity, and keep all the system signals bounded in the adaptive ILC process. Finally, a simulation example is used to demonstrate the feasibility and effectiveness of the adaptive fuzzy ILC scheme.     

Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro‐fuzzy inference system and K‐means clustering

In this paper, a fuzzy expert system based on adaptive neuro‐fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p‐value of less than 0.05 in chi‐square or t‐student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.     

Gradient‐based back‐propagation dynamical iterative learning scheme for the neuro‐fuzzy inference system

In this paper, a gradient‐based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro‐fuzzy system. Premise and consequent parameters of the neuro‐fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output with respect to the structure parameters. The first order derivative of the model output with respect to the structure parameters determines the sensitivity of the model to structure parameters. The sensitivity values are then used to set the tuning factors and parameters updating step sizes. Therefore, an adaptive dynamical iterative scheme is achieved which adapts the learning procedure to the current state of the performance during the optimization process. Larger tuning step sizes make the convergence speed higher and vice versa. In this regard, this parameter is treated according to the calculated sensitivity of the model to the parameter. The proposed learning algorithm is compared with the least square back propagation method, genetic algorithm and chaotic genetic algorithm in the neuro‐fuzzy model structure optimization. Smaller mean square error and shorter learning time are sought in this paper, and the performance of the proposed learning algorithm is versified regarding these criteria.     

An adaptive coherence filter

This paper derives an adaptive coherence filter for canceling interference from a signal of interest whose power spectral density is symmetric. A basic property of the Fourier transform of real signals is that their spectra are Hermitian symmetric. This property is exploited to determine which part of a spectrum is interference and which part is the signal of interest. An unconstrained Wiener filter is derived that exploits the frequency domain symmetry of the signal of interest. While the adaptive coherence filter is based on the Fourier transform property of real signals, an extension of the algorithm is provided so the filter can be used on any signal that displays spectral symmetry. A practical method for implementing the filter is provided. The filter has application in the area of telecommunications, but is applicable in wireless communication applications where a signal, that displays spectral symmetry, is corrupted by interfering signals within the signal of interest's bandwidth.     

地铁车厢灯光自适应控制研究

解决地铁节电问题一直是国内外大力研究的课题。而行驶在高架以及地面段的地铁列车具有天然的采光资源——自然光。根据这一现状,提出一种自适应调节车厢内灯具照度的方法,来达到节约地铁车厢内照明用电的目的。针对自然光随机性高,变化频率快,以及地铁运行时外界环境干扰大的问题,提出采用数字滤波器结合模糊控制的方法,将经巴特沃斯低通滤波器滤除高频干扰的车厢内照度信号传输给模糊控制器,通过模糊控制器的逻辑推理判断处理,实现了地铁车厢灯光自适应地调节车厢内灯具的照度,节约了地铁车厢照明用电。     

Kalman滤波理论在GNSS信号跟踪中的应用

在分析传统GNSS接收机跟踪环路的基础上,对传统的码环滤波和载波滤波在复杂的环境下精度不高的问题,提出一种以自适应Kalman滤波算法代替码环和载波环中的两个环路滤波器,并依据新息自适应的对测量噪音实时调整,将调整结果输入到卡尔曼跟踪环路,估计跟踪误差、实现GNSS(扩频信号)信号的跟踪,提高环路在复杂应用环境下的跟踪精度。仿真结果证明了这种方法提高了跟踪精度,对环境的适应能力有了明显的增强。     

基于修正快速傅里叶变换算法的仪表着陆系统信号干扰抑制与定量分析

针对日益繁忙的机场空域和周边建筑对仪表着陆系统信号干扰的增加,以及传统模拟处理技术缺陷,提出一种基于修正快速傅里叶变换(FFT)频谱校正和最小均方差(LMS)算法自适应滤波器结合实现仪表着陆系统(ILS)信号鉴频分离的技术方案。该方案应用LMS自适应滤波器对ILS信号干扰进行抑制,完成滤波器的权值系数设定,并通过修正FFT技术在时域与频域内对ILS信号进行频谱分离与提取,修正其频谱幅值,以消除由于采样引起的频谱泄露和栅栏效应对信号的影响,使得提取信号逼近理想情况,提高调制度差(DDM)识别精度。针对ILS信号的干扰抑制与频域分离进行了仿真验证,结果表明所提出的信号处理系统技术方案能够有效地对干扰进行抑制,完成信号频域识别,为飞机着陆进近阶段提供准确可靠的导航信息。     

INTELLIGENT MODELING AND CONTROL OF WASHING MACHINE USING LOCALLY LINEAR NEURO‐FUZZY (LLNF) MODELING AND MODIFIED BRAIN EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLER (BELBIC)

Intelligent control of home appliances has, in recent years, attracted much theoretical attention, as well as becoming a major factor for industrial and economic success and rapid market penetration. Washing Machines represent an important market. Intelligent control techniques are capable of providing useful means for both easier use and energy and water conservation. In this paper, the authors use two techniques that have successfully been used in other intelligent modeling and control applications. Firstly, the authors use a neuro‐fuzzy locally linear model tree system for data driven modeling of the machine. Secondly, the authors use a neural computing technique, based on a mathematical model of amygdala and the limbic system, for emotional control of the washing machine. The obtained results indicate the applicability of the proposed techniques in this important business sector.     

神经模糊系统中模糊规则的优选

提出一种基于两级聚类算法的自组织神经模糊系统，该系统采用两级聚类算法（改进的最近邻域聚类算法和Gustafson-Kessel模糊聚类算法）对输入／输出数据进行模糊聚类，并由模糊聚类的划分熵确定最优划分，建立模糊模型，模型精度可由梯度下降法进一步提高。仿真结果表明，这种神经模糊系统具有结构简单、规则数少、学习速度快以及建模精度高等特点。     

基于小波包与模糊聚类算法的声波法检测流化床内颗粒的平均粒度

平均粒径是气固流化床反应器运行时需要监控的重要参数之一,利用声波信号检测床内颗粒平均粒度的方法能克服传统方法不能实时在线测量的缺陷,安全环保不侵入流场.先用Db5小波包将声发射信号3尺度分解,求出各细节信号小波系数的绝对值加和,构成声信号的能量模式,标准化之后经主成分分析得出主成分,再用模糊均值聚类方法分类.由于不同粒度的声波信号经小波包分解后,其小波系数绝对值加和具有特定的模式,因而,这种方法分类准确性达98%以上.     

A fast expert system for electrocardiogram arrhythmia detection

Abstract: A fast expert system for electrocardiogram (ECG) arrhythmia detection has been designed in this study. Selecting proper wavelet details, the ECG signals are denoised and beat locations are detected. Beat locations are later used to locate the peaks of the individual waves present in each cardiac cycle. Onsets and offsets of the P and T waves are also detected. These are considered as ECG features which are later used for arrhythmia detection utilizing a novel fuzzy classifier. Fourteen types of arrhythmias and abnormalities can be detected implementing the proposed procedure. We have evaluated the algorithm on the MIT–BIH arrhythmia database. Application of the wavelet filter with the scaling function which closely resembles the shape of the ECG signal has been shown to provide precise results in this study.     

Direct adaptive fuzzy control of nonlinear strict-feedback systems

This paper focuses on adaptive fuzzy tracking control for a class of uncertain single-input /single-output nonlinear strict-feedback systems. Fuzzy logic systems are directly used to approximate unknown and desired control signals and a novel direct adaptive fuzzy tracking controller is constructed via backstepping. The proposed adaptive fuzzy controller guarantees that the output of the closed-loop system converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. A main advantage of the proposed controller is that it contains only one adaptive parameter that needs to be updated online. Finally, an example is used to show the effectiveness of the proposed approach.     

Direct adaptive fuzzy control for nonlinear systems with time-varying delays

This paper focuses on the problem of direct adaptive fuzzy control for nonlinear strict-feedback systems with time-varying delays. Based on the Razumikhin function approach, a novel adaptive fuzzy controller is designed. The proposed controller guarantees that the system output converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. Different from the existing adaptive fuzzy control methodology, the fuzzy logic systems are used to model the desired but unknown control signals rather than the unknown nonlinear functions in the systems. As a result, the proposed adaptive controller has a simpler form and requires fewer adaptation parameters.