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.     

Change detection in synthetic aperture radar images based on unsupervised artificial immune systems

In this paper, we propose a novel change detection method for synthetic aperture radar images based on unsupervised artificial immune systems. After generating the difference image from the multitemporal images, we take each pixel as an antigen and build an immune model to deal with the antigens. By continuously stimulating the immune model, the antigens are classified into two groups, changed and unchanged. Firstly, the proposed method incorporates the local information in order to restrain the impact of speckle noise. Secondly, the proposed method simulates the immune response process in a fuzzy way to get an accurate result by retaining more image details. We introduce a fuzzy membership of the antigen and then update the antibodies and memory cells according to the membership. Compared with the clustering algorithms we have proposed in our previous works, the new method inherits immunological properties from immune systems and is robust to speckle noise due to the use of local information as well as fuzzy strategy. Experiments on real synthetic aperture radar images show that the proposed method performs well on several kinds of difference images and engenders more robust result than the other compared methods.     

Contact temperatures and their influence on wear during pin-on-disk tribotesting

It is important to take contact temperatures into account when developing friction and wear tests for potential tribomaterials and when analyzing the results of those tests. This paper presents some of the most useful analytical and numerical methods that can be used to predict surface temperature rises in dry or boundary lubricated pin-on-disk tribotests. The objective is the development of relatively simple, accurate, and easy-to-use expressions that can be used to predict contact temperatures in pin-on-disk sliding contacts. Results of the methods are compared for several different cases, and experimental verification of the predictions are also presented. The resulting expressions are applied to investigate wear of a ceramic (zirconia), metal (stainless steel) and polymer (polyethylene) in pin-on-disk tests.     

A probabilistic load shedding concept considering highly volatile local generation

Automatic load shedding is the ultimate countermeasure against imbalance in a power system and can effectively help preventing large blackouts. Taking into account a high penetration of renewable energy sources (RES) in the distribution grid, a clear distinction between load and generation at the PCC becomes increasingly more difficult. For that reason an adaptation of frequency relay parameters and their locations of installation are necessary. In Europe this is rest on a multi-step plan based on values such as the yearly peak load. In this paper a novel probabilistic method for automatic load shedding is presented that uses the average values instead of peak values for load shedding. Its applicability is verified by a dynamic power system model that was developed to compare the classical and novel probabilistic load shedding principle. The method is verified using data from a German TSO.     

Real-Time Duplex Applications of Loop-Mediated AMPlification (LAMP) by Assimilating Probes

Isothermal nucleic-acid amplification methods such as Loop-Mediated isothermal AMPlification (LAMP) are increasingly appealing alternatives to PCR for use in portable diagnostic system due to the low cost, weight, and power requirements of the instrumentation. As such, interest in developing new probes and other functionality based on the LAMP reaction has been intense. Here, we report on the development of duplexed LAMP assays for pathogen detection using spectrally unique Assimilating Probes. As proof of principle, we used a reaction for Salmonella enterica as a model coupled with a reaction for λ-phage DNA as an internal control, as well as a duplexed assay to sub-type specific quarantine strains of the bacterial wilt pathogen Ralstonia solanacearum. Detection limits for bacterial DNA analyzed in individual reactions was less than 100 genomic equivalents in all cases, and increased by one to two orders of magnitude when reactions were coupled in duplexed formats. Even so, due to the more robust activity of newly available strand-displacing polymerases, the duplexed assays reported here were more powerful than analogous individual reactions reported only a few years ago, and represent a significant advance for incorporation of internal controls to validate assay results in the field.     

Study of MW-scale biogas-fed SOFC-WGS-TSA-PEMFC hybrid power technology as distributed energy system: Thermodynamic,exergetic and thermo-economic evaluation

Advanced biogas power generation technology has been attracting attentions, which contributes to the waste disposal and the mitigation of greenhouse gas emissions. This work proposes and models a novel biogas-fed hybrid power generation system consisting of solid oxide fuel cell, water gas shift reaction, thermal swing adsorption and proton exchange membrane fuel cell (SOFC-WGS-TSA-PEMFC). The thermodynamic, exergetic, and thermo-economic analyses of this hybrid system for power generation were conducted to comprehensively evaluate its performance. It was found that the novel biogas-fed hybrid system has a gross energy conversion efficiency of 68.63% and exergy efficiency of 65.36%, indicating high efficiency for this kind of hybrid power technology. The market sensitivity analysis showed that the hybrid system also has a low sensitivity to market price fluctuation. Under the current subsidy level for the distributed biogas power plant, the levelized cost of energy can be lowered to 0.02942 $/kWh for a 1 MW scale system. Accordingly, the payback period and annual return on investment can reach 1.4 year and about 20%, respectively. These results reveal that the proposed hybrid system is promising and economically feasible as a distributed power plant, especially for the small power scale (no more than 2 MW).     

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.     

基于学生群体心理测试系统的设计

心理测试的根本目的是为了让人们在测试过程中，产生某些特定行为，即个体对测试题目的反应，并根据这些行为反应来推论其相应的心理特性。在一定程度上帮助我们了解人类的心理活动，并从中得到帮助。因此，本文对基于学生群体心理测试系统的设计进行深入研究。     

GK1C型内燃机车频繁停车现象探讨与解决方案

运用过程中各类相关因子的变化,会使得机车柴油机配气相位出现一定程度变化,采取某种措施调整或是处理可以解决动力不足的问题。