Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

一种改进的新三步搜索算法

在对三步法工作原理进行研究的基础上提出了新三步搜索算法（NITSS）。该方法充分利用视频序列运动矢量概率分布上的中心偏置特性．在三步搜索算法的基础上引入了十字型分布的4个点构成搜索点群。实验结果表明。新三步搜索算法解决了三步法的小运动估计效果较差问题．提高了搜索精度，保持了三步法的高效率。     

Performance discrepancies on the California Verbal Learning Test-Second Edition (CVLT-II) in the standardization sample.

The standardization data for the California Verbal Learning Test-Second Edition (CVLT-II; D. C. Delis, J. H. Kramer, E. Kaplan, & B. A. Ober, 2000) were used to evaluate the base rate of 6 specific discrepancies between various key variables. The results indicated that CVLT-II performance discrepancies should equal or exceed 1 or 1.5 z score points (depending on the individual comparison) in the hypothesized direction to be considered potentially unusual. However, because about 1 in 3 persons in the standardization sample displayed at least 1 such large discrepancy, it is concluded that these base rates should be viewed only as a starting point for interpretation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An effective coding approach for multiobjective integrated resource selection and operation sequences problem

In this paper, we consider an integrated Resource Selection and Operation Sequences (iRS/OS) problem in Intelligent Manufacturing System (IMS). Several kinds of objectives are taken into account, in which the makespan for orders should be minimized; workloads among machine tools should be balanced; the total transition times between machines in a local plant should also be minimized. To solve this multiobjective iRS/OS model, a new two vectors-based coding approach has been proposed to improve the efficiency by designing a chromosome containing two kinds of information, i.e., operation sequences and machine selection. Using such kind of chromosome, we adapt multistage operation-based Genetic Algorithm (moGA) to find the Pareto optimal solutions. Moreover a special technique called left-shift hillclimber has been used as one kind of local search to improve the efficiency of our algorithm. Finally, the experimental results of several iRS/OS problems indicate that our proposed approach can obtain best solutions. Further more comparing with previous approaches, moGA performs better for finding Pareto solutions. Received: May 2005/Accepted: December 2005     

马钢二烧结配料计算机控制系统

阐明了马钢二烧结配科计算机控制系统的目的、控制形式、控制原理；根据烧结配料控制对象的特点对信号采样和控制算法进行了探讨；提供了系统的主要硬件结构，软件功能和程序框图。     

基于交会图和多元统计法的神经网络技术在火山岩识别中的应用

针对火山岩储层的特殊性(复杂性、离散性和随机性),应用BP神经网络技术对火山岩测井解释中岩性识别问题进行了研究。该方法的技术关键是样本集和初始权重的建立,以及模型的优选。本文提出了一种基于交会图和多元统计法的学习样本生成方法,即根据取心岩样的地球化学和岩石学研究,用交会图技术建立样本集,采用聚类分析和距离判别法确定初始权重。将研究方法应用在松辽盆地杏山地区火山岩岩性识别问题中,取得了很好的效果,岩性解释符合率高于90%。文中通过四种岩性识别处理模式的对比研究,表明赋权重处理模式为最优处理模型。在神经网络模型预测过程中,需充分利用已有的地质经验和测井曲线信息建立典型可靠的样本文件,同时考虑神经网络方法中各种因素的影响,优选模型和计算参数才能使预测结果符合实际情况。     

A Least Squares Approach to Escalator Algorithms for Adaptive Filtering

In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization property of the ESC structure. From the simulation results, the proposed algorithm shows superior convergence performance.     

USB数据传输中CRC校验码的并行算法实现

文章介绍了用于USB总线数据传输的CRC校验的原理和算法，并且采用并行电路实现USB2．0中的CRC产生和CRC校验，与传统的串行电路实现相比，并行电路实现方法虽然在芯片面积上大于串行电路实现，但由于降低了时钟频率，电路更容易综合实现，并且大大降低了功耗，有利于低功耗电路设计。     

基于进化算法的常减压装置模拟

提出了基于优选优生进化算法（Select—best and prepotency evolution algorithm，SPEA）的常减压装置模拟方法。以各塔的温度分布为目标，基于生产过程和分析数据，采用SPEA确定各塔板Murphree效率，从而精确描述各塔板平衡偏离程度。在实际应用中，获得具有良好精度的常减压装置模型。系统分析了SPEA算法的关键参数——优选领域大小与计算复杂性及寻优性能之间的关系，结果显示其取值为群体规模的10％最为合适。