移动加热源法制备单层硒化镓晶体

为了解决硒化镓(GaSe)晶体制备困难、化学性能差的问题,对GaSe晶体传统的化学汽相沉积制备方法进行了改进,采用移动加热源法制备GaSe晶体。搭建了GaSe晶体的制备装置,通过单片机精确调控制备晶体的高温炉的加热温度、移动位置等参数,并采用光学显微镜和原子力显微镜对所制备的GaSe晶体进行辅助表征。研究表明,利用移动加热源法可以制备出表面光滑且尺寸较大的单层二维GaSe晶体。由于对机电设备实现了自动精密移动,可对单层二维GaSe晶体实现高质量大批量的制备,有利于GaSe晶体在光电子学和纳电子学中的广泛应用。     

基于SARIMA-GRNN-SVM的短期商业电力负荷组合预测方法

针对短期商业电力负荷预测准确性与周期难以满足现有电力现货市场的问题,提出了一种基于SARIMAGRNN-SVM(seasonal autoregressive integrated moving average-generalized regression neural network-support vector machine)的商业电力负荷组合预测模型。首先,对商业电力负荷变化的周期规律与随机因素的复杂影响进行了分析;然后,结合以上分析,选用SARIMA和GRNN为单一预测模型对商业电力负荷进行预测,并利用SVM进行组合,实现日前商业电力负荷预测;最后,通过某商业综合体的电力负荷数据进行验证。所提组合预测模型较单一预测模型拥有更优的预测精度与鲁棒性,可以为短期商业电力负荷预测提供借鉴。     

A signal adaptive diffusion filter for video coding: Mathematical framework and complexity reductions

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.     

A hybrid homogeneously weighted moving average control chart for process monitoring

Several modifications and enhancements to control charts in increasing the performance of small and moderate process shifts have been introduced in the quality control charting techniques. In this paper, a new hybrid control chart for monitoring process location is proposed by combining two homogeneously weighted moving average (HWMA) control charts. The hybrid homogeneously weighted moving average (HHWMA) statistic is derived using two smoothing constants λ₁ and λ₂ . The average run length (ARL) and the standard deviation of the run length (SDRL) values of the HHWMA control chart are obtained and compared with some existing control charts for monitoring small and moderate shifts in the process location. The results of study show that the HHWMA control chart outperforms the existing control charts in many situations. The application of the HHWMA chart is demonstrated using a simulated data.     

Content adaptive video denoising based on human visual perception

In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures.     

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.     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

Robust Mapping for Mobile Robot Based on Immobile Area Grid Map Considering Potential Moving Objects

Mobile robots need Simultaneous Localization and Mapping (SLAM) for autonomous movement in human living environments. The occupancy grid map used in SLAM is a conventional method which makes a map by an occupancy probability in each grid. This method renews a map based on whether an object is observed or not. In order to remove moving objects from a map, an additional method is required. However, conventional methods deal only with actually moving objects, and potential moving objects (e.g., standing humans) are mapped as static objects. Furthermore, only binary states, used or not used, are given to each object in map updating. This paper proposes the immobility area grid map to represent a map by an immobility probability in each grid. The proposed method renews a map based on the identification of observed objects by a robot's sensors, in addition to whether an object is observed or not. We introduce the map update parameter, which is set adaptively from the certainty of identification result of the object. Observed objects can take continuous states, truly static—unknown—truly moving, according to the parameter value. Potential moving objects are not mapped if the parameter takes values corresponding to moving objects. The experimental results show robust mapping in dynamic environments including potential moving objects.     

Improved average consensus algorithm based distributed cost optimization for loading shedding of autonomous microgrids

This paper addresses a new distributed cost optimization (DCO) method for load shedding (LS) of an islanded microgrid considering cost. A two-layer improved average consensus algorithm (IACA) of multi-agent system (MAS) is proposed, and the consensus characteristic of which is analyzed in detail. With the global information discovered in the first layer of the IACA, the DCO of LS can be solved by using the synchronization processing of the IACA in the second layer. PSCAD/EMTDC-based simulation models are built to study the value settings of consensus constants and the performances of the proposed DCO method. Simulation results verified the convergence improvement of the IACA and the effectiveness of the proposed DCO.     

机动小目标的时空联合检测技术

主动声呐信号处理中一般采用匹配滤波和背景均衡等算法检测回波信号,为提高信号的检测性能,还需利用信号与混响和环境噪声在时间、空间上的统计特征差异。文章在对信号的时、空特征算法进行简单描述的基础上,提出了频率能量统计特征和方位能量统计特征,将两种特征检测器的输出进行融合处理构成时、空联合检测器。仿真和试验数据处理结果证明,此方法可有效提高机动小目标在混响及环境噪声中的检测性能。