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.     

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.     

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.     

信息安全风险评估的模糊多准则决策方法

基于ISO/IEC17799标准建立了一个综合的信息系统风险分析框架,并运用模糊多准则决策(FMCDM)方法计算信息安全风险,根据风险等级矩阵(RLM)对信息资产风险进行级别划分,最终建立评估信息资产相关风险的完整模型。     

浅谈在多节目码流复用与解复用中PID码的作用

文章简要阐述了PID码在多节目码流的复用与解复用中所起的作用。并运用PID码的原理来解释和解决在实际工作中碰到的PID码问题。     

饼干炉湿度控制的改进方法

在大空间的温度控制系统中 ,采用简单的PID控制规律 ,对空间的温度、湿度进行解藕计算 ,将空间的相对湿度控制转化为送风含湿量的控制。该控制方式稳定、可靠 ,并且节约能源。本方法适用于所有的烘烤炉中     

双波长稳频CO_2激光模糊控制系统的建模与优化

报导一种模糊逻辑控制系统的建模与优化方法。以此方法设计的模糊逻辑控制器，用于双波长稳频ＣＯ２激光器的控制得到令人满意的结果。     

真空冻干生产线模糊控制系统

介绍了采用模糊控制技术解决真空冻干生产线控制中大超调量、大滞后等问题的方法 ,详述了模糊控制系统的构成、主要功能和实现方法     

模糊数学识别火山岩岩性

针对火岩地层岩性识别成功率低的状况，应用模糊数学原理，建立模糊数学识别火山岩岩性模式，识别和划分火山岩地层岩性，以便进一步深入研究火山岩油气藏储层，选择8个反映火山岩岩性测井参数，确定阈值；利用所建立的火山岩岩性识别模式，对火山岩岩性进行分类；依据最大隶属原则，识别火山岩岩性，应用实地研究资料，识别10种火山岩岩性，并与岩心鉴定统计分析结果对比，一致性达85％。表明该方法识别火山岩岩性精度较高，在地质上具有很好的应用前景。     

Extrapolation of fuzzy values from incomplete data bases

This paper presents different approaches which enable a data base management system to obtain a plausible fuzzy estimate for an attribute value of an item for which the information is not explicitly stored in the data base. This can be made either by a kind of analogical reasoning from information about particular items or by means of expert rules which specify the (fuzzy) sets of possible values of the attribute under consideration, for various classes of items. Another kind of expert rules enables the system to compute an estimate from the attribute value of another item provided that, in other respects, this latter item sufficiently resembles the item, the value of which we are interested in; then these expert rules are used either for controlling the analogical reasoning process or for enlarging the scope of application of the first kind of expert rules. The different approaches are discussed in the framework of possibility theory.