Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

Study on maximum power point tracking of photovoltaic array in irregular shadow

Traditional maximum power point tracking (MPPT) methods can hardly find global maximum power point (MPP) because output characteristics curve of photovoltaic (PV) array may have multi local maximum power points in irregular shadow, and thus easily fall into the local maximum power point. To address this drawback, Considering that sliding mode variable structure (SMVS) control strategy have such advantages as simple structure, fast response and strong robustness, and P&O method have the advantages of simple principle and convenient implementation, so a new algorithm combining SMVS control method and P&O method is proposed, besides, PI controller is applied to reduce system chattering caused by switching sliding surface. It is applied to MPPT control of PV array in irregular shadow to solve the problem of multi-peak optimization in partial shadow. In order to verity the rationality of the proposed algorithm, the experimental circuit is built, which achieves MPPT control by means of the proposed algorithm and P&O method. The experimental results show that compared with the traditional P&O algorithm, the proposed algorithm can fast track the global MPP, tracking speed increases by 60% and the relative error decreased by 20%. Moreover, the system becomes more stable near the MPP, the fluctuations of output power is greatly reduced, and thus make full use of solar energy.     

The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net

The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing.     

Prediction of rock mass P wave velocity using blasthole drilling information

In this paper, the development of the models for the prediction of rock mass P wave velocity is presented. For model development, the database of 53 cases including widely used and recorded drilling parameters and P wave velocity was constructed from the field studies conducted in 13 open pit lignite mines. Both conventional linear, non-linear multiple regression and Adaptive Neuro Fuzzy Inference System (ANFIS) were used for model development. Prediction performance indicators showed that ANFIS model presented the best performance and it can successfully be used for the preliminary prediction of P wave velocities of rock masses.     

Type II fuzzy set-based data analytics to explore amino acid associations in protein sequences of Swine Influenza Virus

The veracity present in molecular data available in biological databases possesses new challenges for data analytics. The analysis of molecular data of various diseases can provide vital information for developing better understanding of the molecular mechanism of a disease. In this paper, an attempt has been made to propose a model that addresses the issue of veracity in data analytics for amino acid association patterns in protein sequences of Swine Influenza Virus. The veracity is caused by intra-sequential and inter-sequential biases present in the sequences due to varying degrees of relationships among amino acids. A complete dataset of 63,682 protein sequences is downloaded from NCBI and is refined. The refined dataset consists of 26,594 sequences which are employed in the present study. The type I fuzzy set is employed to explore amino acid association patterns in the dataset. The type I fuzzy support is refined to partially remove the inter-sequential biases causing veracity in data. The remaining inter-sequential biases present in refined fuzzy support are evaluated and eliminated using type II fuzzy set. Hence, it is concluded that a combination of type II fuzzy & refined fuzzy approach is the optimal approach for extracting a better picture of amino acid association patterns in the molecular dataset.     

重力坝坝基多斜面抗滑稳定模糊体系可靠度研究

以基岩具有多斜面、多滑移通道的某实际重力坝工程为例,基于刚体极限平衡法,采用模糊理论和几种典型的体系可靠度计算方法,研究其典型坝段的坝基深层抗滑稳定模糊体系可靠度,探讨模糊理论中隶属函数形式、隶属度以及隶属函数中关键参数的取值对可靠度计算结果的影响。研究结果表明,采取限制失效概率模糊界限比的方法可使计算结果更具有参考性,考虑模糊性的抗滑稳定可靠指标的下限较常规可靠度计算值偏小,这在重力坝坝基抗滑稳定分析中应引起重视。     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

Adaptive fuzzy control for nontriangular form systems with time-varying full-state constraints

This article investigates an adaptive fuzzy tracking control problem for a class of nontriangular form systems with asymmetric time-varying full state constraints. Unknown functions are approximated by the fuzzy logic systems. A domination approach is employed to tackle the nontriangular form structure. Time-varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure full-state constraints satisfaction. Based on the backstepping technique and time-varying ABLFs, an adaptive controller is proposed and guarantees that all the signals in the closed-loop system are ultimately bounded and the time-varying full state constraints are met. Simulation examples are presented to further demonstrate the effectiveness of the proposed approach.     

熵模糊物元分析法在水库正常高水位决策中的应用

基于信息熵理论和模糊物元分析理论,探讨了水库正常高水位的熵模糊物元决策模型.以待选方案与最优方案间的海明贴近度来决策水库正常高水位.在确定评价综合指标信息熵的熵权和专家知识权重的的过程中,充分挖掘待选方案的固有信息和避免个人主观因素的弊端.实例应用表明,该方法有效、可行.     

基于多Agent的模糊粗交流

提出了变异模糊粗集的概念及其相应的性质和定理;建立了模糊粗交流最优传递序列的数学模型,并给出了解决此模型的算法思想,算例证明了算法思想的有效性。