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.     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

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.     

离心泵并联运行工况点的数值求解

建立了求解多台不同型号离心泵并联运行工况点的数学模型,给出了用数值计算中的“二分法”求解该模型的方法,并附有用Exce l计算的实例。     

XMAC纵波时差高分辨率处理方法

在充分调研阵列声波测井分波提取方法的基础上，针对阿特拉斯公司的交叉多极子阵列声波测井（XMACⅡ）提供的单极波形信息，研究纵波时差提取及高分辨率处理方法。首先利用相关-互功谱法高精度地提取纵波时差，然后针对XMACⅡ仪器的接收阵列，研究组合子阵列的方法，形成共发射子阵列和共接收子阵列，最后利用子阵列对提取的纵波时差做高分辨率处理。处理现场实际测井资料，并将高分辨率处理成果与引进的eXpress软件处理成果、实测的国产高分辨率声波（纵波）测井曲线及岩心纵波时差对比分析，表明该方法提取的纵波时差曲线的纵向分辨率有很大提高，时差计算准确可靠，提取的单极纵波时差与国产高分辨率声波时差及岩心纵波时差的平均相对误差均小于7%。该方法具有较高的实用价值和广阔的应用前景。     

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。     

腐蚀阴极尖端的新方法

给出一种腐蚀阴极尖端的新方法,使得对阴极常用金属材料(W,Mo,Ir,Ta等)制作的阴极尖端的腐蚀效果明显,针尖对称性和重复性都很好,成品率和质量都有所提高.     

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。     

邻—(4—乙基苯甲酰基)苯甲酸的研究

本文阐述了通过Friedel-Crafts反应合成邻-(4-乙基苯甲酰基)苯甲酸的方法.研究了影响产物质量的因素,提出了适宜的工艺条件.