Service-oriented smart home applications: composition, code generation, deployment, and execution

A smart home usually has a variety of devices or home appliance, instead of designing software for a specific home, this paper proposes a service-oriented framework with a set of ontology systems to support service and device publishing, discovery of devices and their services, composition of control software using existing control services that wrap devices, deployment, and execution of the composed service in an computing environment, monitoring the execution, and recovery from device failure. The ontology systems specify semantic information about devices, services, and workflows used in various smart home, and users can compose and recompose services for their specific needs. New devices, workflows, and services can be added into ontology. Most of the steps in this process can be automated including code generation. For example, service composition will be carried out in three steps: abstract workflow design, function construction, and device discovery, and different codes can be generated for different computing platforms such as Java and Open Services Gateway initiative environments. In this way, a variety of smart home can be constructed rapidly using the framework by discovery and composition using existing services and workflows. This paper illustrates this framework using a media control example to illustrate the ontology, discovery, composition, deployment, execution, monitoring, and recovery.     

信息技术课堂有效管理的策略探索

随着社会信息化的发展,信息素养日益成为信息社会公民素养不可或缺的组成部分,对课堂管理提出了更高的要求。信息技术课程具有独特的学科特点以及特有的教学环境,给课堂的管理带来了困难和不便。从建立课堂常规、固定上机位置、设置门禁门槛等方面,探索了信息技术课堂有效管理的方法。     

济南市1972─2016年降水特性

运用线性滑动平均、Mann-Kendall非参数趋势检验、Sen′s坡度、小波分析及多元线性回归方法,选取济南市24个降水站点45年(1972—2016年)逐日降水资料,分析了济南市在年和季节尺度上的降水特征以及降水量的决定性影响因素。结果表明:年降水量以8.23mm/(10a)的速率增加,且呈现出丰枯交替的变化周期,最大主周期为10a;降水量主要集中在夏季,占全年降水量的64.5%,冬季降水占比最小,占全年降水量的3.4%,春、夏和冬季降水具有不显著增加趋势,秋季的降水量具有不显著下降趋势;降水量的年际、春夏季变化主要受降水强度变化的影响;秋冬季变化则主要受降水频率变化的影响。     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation and Registration Nets” (ASRNet) for the simultaneous estimation of the blood vessel segmentation and the registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills the blood vessel segmentation task, and (ii) A registration module R that estimates the spatial correspondence of an image pair. Based on the segmention-driven registration network, we train the segmentation network using a semi-supervised adversarial learning strategy. Our experimental results show that the proposed ASRNet can achieve state-of-the-art accuracy in segmentation and registration tasks performed with real MSI datasets.     

A low distortion image enhancement scheme based on multi-resolutions analysis in next generation network

With the advent of Next Generation Network (NGN), services that are currently provided by multiple specific network-centric architectures. NGN provides AAA (Anytime, Anywhere and Always on) access to users from different service providers with consistent and ubiquitous provision of services as necessary. This special issue of NGN includes pervasive, grid, and peer-to-peer computing to provide computing and communication services at anytime and anywhere. In fact, the application of NGN includes digital image processing, multimedia systems/services, and so on. Here we focus on the digital image processing technology in NGN environments. Low-contrast structure and heavy noise in NGN environments can be found in many kinds of digital images, which makes the images vague and uncertainly, especially in x-ray images. As result, some useful tiny characteristic are weakened—which are difficult to distinguish even by naked eyes. Based on the combination of no-linear grad-contrast operator and multi-resolution wavelet analysis, a kind of image enhancement processing algorithm for useful tiny characters is presented. The algorithm can enhance the tiny characters while confine amplifying noise. The analysis of the results shows that local regions of the image are enhanced by using the concept of the grad contrast to make image clearer adaptively. Experiments were conducted on real pictures, and the results show that the algorithm is flexible and convenient.     

Robust stability and stabilization of discrete singular systems with interval time-varying delay and linear fractional uncertainty

The robust stability and robust stabilization problems for discrete singular systems with interval time-varying delay and linear fractional uncertainty are discussed. A new delay-dependent criterion is established for the nominal discrete singular delay systems to be regular, causal and stable by employing the linear matrix inequality (LMI) approach. It is shown that the newly proposed criterion can provide less conservative results than some existing ones. Then, with this criterion, the problems of robust stability and robust stabilization for uncertain discrete singular delay systems are solved, and the delay-dependent LMI conditions are obtained. Finally, numerical examples are given to illustrate the e?ectiveness of the proposed approach.     

IETM技术在便携式工卡系统中的应用研究

从飞机制造的角度出发,以IETM技术为基础,以带Windows 7操作系统的便携式终端为硬件平台,研究开发应用于某机机载机电设备装配指导的交互式电子工卡系统。文中主要介绍了系统设计的背景知识、开发架构和软硬件实现方法,结合XML和SQL数据库技术创作符合GJB6600标准的应用系统。该系统的成功研制和投入使用可将电子信息技术的最新研究成果用于生产实践,既可有效地减轻生产一线工作人员的劳动强度,又能极大地提高飞机数字化制造效率。     

16MnR钢高压除氧器焊接接头裂纹的成因

本文采用电镜，Ｘ射线衍射分析、裂纹扩展速率ｄａ／ｄＮ及门槛值△Ｋｔｈ试验，研究了火力发电厂１６ＭｎＲ钢高压除氧器焊接接头裂纹的形成原因，结果表明，原发裂纹为延迟裂纹，运行工况下，裂纹以应力腐蚀裂纹方式扩展。     

Boosting the Microbial Electrosynthesis of Acetate from CO2 by Hydrogen Evolution Catalysts of Pt Nanoparticles/rGO

Catalysis Letters - Microbial electrosynthesis (MES) is an effective approach to driving the CO2 reduction to multi-carbon organic products using renewable energy. In this work, the MES of acetate...