Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

基于学科竞赛的中医药类院校程序设计课程教学模式的探索

高素质人才培养是我国可持续发展战略、科教兴国战略以及人才强国战略等多项发展战略的一项基础性工程。本文针对中医药类院校程序设计类课程实践教学模式单一等教学实际，以“学科竞赛”为平台，“赛教结合”为理念，以《C语言程序设计》为例，从现存问题、赛教结合模式的引入、建立实施及其评价机制4个方面展开阐述，旨在提高学生学习积极性和能力。     

Designed damping controller for SSSC to improve stability of a hybrid offshore wind farms considering time delay

This paper presents the stability improvement results of hybrid doubly fed induction generator (DFIG)-based and permanent magnet generator (PMG)-based offshore wind farms (OWFs) using a static synchronous series compensator (SSSC). An adaptive-network-based fuzzy inference system (ANFIS) controller of the proposed SSSC is designed to render adequate damping characteristics to the studied system. A frequency-domain approach based on a linearized system model using eigenvalue technique analysis is performed. A time-domain scheme based on a nonlinear system model subject to a three-phase short circuit fault at infinite bus with variations in the signal transmission delays has also been investigated to compare the damping of the studied system in cases of with and without controller. The simulation results with MATLAB/SIMULINK toolbox have been presented. It can be concluded from the simulation results that the proposed SSSC joined with the designed ANFIS damping controller can offer adequate damping performance to the studied hybrid DFIG-based and PMG-based OWFs under severe disturbance.     

Affective matches of fabric and lighting chromaticity

This study investigates the impact of lighting colors on subjective judgments of fabric: in particular, whether the influence of lighting varies depending on fabric types and color combinations. We conducted two visual assessments. In Study 1 (N = 44), eight illuminants and six types of fabric were presented as cloth stimuli. Derived from the literature review, four sets of adjectives (humble-luxurious, cool-warm, old-new, and not preferred-preferred) were used as metrics. In Study 2 (N = 41), five sets of fabric color combination swatches were assessed under lighting conditions that were identical to those of Study 1. Three bipolar scales (ordinary-characterful, classic-modern, and soft-rigid), were employed from factor analysis along with three unipolar scales (luxurious, preferred, harmonious with lighting). The results showed that hue characteristics of lighting and cloth types influenced participants' perceptions of the fabric. Overall, white lighting with 4000 K was the most preferred and luxurious lighting across various types of clothes, while a pinkish white with 4700 K (duv = −0.0127) was the best matched in every color combination. In addition, there were interaction effects between lighting colors, clothes types, and fabric color combinations with regard to each of the perceptual qualities. This study provides empirical evidence for optimally match lighting colors with fabric in the presentation of fabric goods.     

极限工况下的管网“互联互通”改进方案

在全国天然气管道“主干互联、区域成网”(以下简称“互联互通”)基础格局逐渐形成的背景下,天然气管网规模日益扩大、管道分支和气源增加,并且分布不集中、输送方向可变,使得输气方案更加灵活,可以更好地解决某些地域的供气紧张问题;但受现有站场和设备的限制,暂不能满足某些多线组合极限工况,使得“互联互通”的初衷难以全部实现。为了使得现有的各输气干线在实现“互联互通”之后可以满足更多的多线组合工况,在分析“互联互通”背景下M管网工况变化的基础上,研发了可以进行水力仿真和压气站方案制订的计算软件,并对3种极限工况下的不同输气量情况进行了可行性试算,进而基于试算结果提出了相应的管网改进建议。研究结果表明:(1)经验证,软件计算误差满足要求;(2)在M管道某处增设压气站或在某些输气站场配置压缩机组;(3) M管网改进调整后,可以完成大部分的多线组合极限工况,真正实现“互联互通”的输气方案。结论认为,该研究成果有助于推进全国天然气管网早日实现“互联互通”。     

Stress distribution in thick-walled cylinder due to non-uniform radial pressure on the example of reinforcement corrosion in concrete

This paper presents an analytical solution to the non-uniform pressure on thick-walled cylinder. The formulation is based on the linear elasticity theory (plain strain) and stress function method. As an example, the proposed solution is used to model the stress distribution due to non-uniform steel reinforcement corrosion in concrete. The model is formulated considering different scenarios of corrosion pressure distribution. It is validated against the finite element model for different cases of non-uniform pressure distributions. The results show that the corrosion-induced cracks are likely to start just beyond the anodic zone. This is confirmed by the experimental tests on concrete cylinder exposed to non-uniform accelerated corrosion of steel reinforcement. The model can be effectively used to calculate the distribution of corrosion-induced stresses in concrete.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.