Preventive replacement policies with time of operations,mission durations,minimal repairs and maintenance triggering approaches

When a mission arrives at a random time and lasts for a duration, it becomes an interesting problem to plan replacement policies according to the health condition and repair history of the operating unit, as the reliability is required at mission time and no replacement can be done preventively during the mission duration. From this viewpoint, this paper proposes that effective replacement policies should be collaborative ones gathering data from time of operations, mission durations, minimal repairs and maintenance triggering approaches. We firstly discuss replacement policies with time of operations and random arrival times of mission durations, model the policies and find optimum replacement times and mission durations to minimize the expected replacement cost rates analytically. Secondly, replacement policies with minimal repairs and mission durations are discussed in a similar analytical way. Furthermore, the maintenance triggering approaches, i.e., replacement first and last, are also considered into respective replacement policies. Numerical examples are illustrated when the arrival time of the mission has a gamma distribution and the failure time of the unit has a Weibull distribution. In addition, simple case illustrations of maintaining the production system in glass factories are given based on the assumed data.     

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.     

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).     

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。     

Application of chitosan–alginate microspheres for the sustained release of bacteriophage in simulated gastrointestinal conditions

This study was designed to evaluate the acid stability, release property and antimicrobial efficacy of Escherichia coli O157:H7 bacteriophages encapsulated in chitosan–alginate microspheres under the simulated gastrointestinal conditions. The bacteriophages belonging to Myoviridae family were stable at the pH above 4 in trypticase soy broth. The chitosan–alginate microspheres exhibited protective effect on the viability of bacteriophages in the simulated gastric conditions at pH 2.0 and pH 2.5, showing 4.8 and 5.6 log PFU mL^‐1, respectively, after 1 h of incubation at 37 °C. The release per cent of bacteriophages from microspheres gradually increased up to 65% in the simulated intestinal condition (pH 7.5) at 37 °C for 6 h. The lytic efficacy of chitosan‐ and alginate‐encapsulated bacteriophages against E. coli O157:H7 was significantly maintained in the simulated intestinal conditions to 10 h of incubation (1.3 log reduction). The results suggest that the chitosan–alginate microspheres can be used as a reliable delivery system for bacteriophages.     

嵌入式盲文单手输入系统设计与实现

计算机录入编辑盲文是信息处理的特殊应用领域，是特殊教育中的重要研究课题。文中将盲文制作为特殊符号，通过制作字库，编写个性化码表，然后嵌入到主流输入法，从而实现盲文与汉字混排以及实现单手盲文输入。该系统具有易学易记性、盲文编码多样性、嵌入性强等优点，并通过实验证明输入盲文效率能提高5~6倍，在盲文出版、盲文印刷、盲文教学等领域有重要的应用价值。但盲文字符在不同平台(如智能手机)与不同操作系统兼容性问题还有待进一步研究开发。     

Time-series clustering – A decade review

Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets. In case of huge datasets, using supervised classification solutions is almost impossible, while clustering can solve this problem using un-supervised approaches. In this research work, the focus is on time-series data, which is one of the popular data types in clustering problems and is broadly used from gene expression data in biology to stock market analysis in finance. This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works.     

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.     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.