超高精度计算程序设计实例

针对工程实践中时常出现超高精度计算的程序设计需求,分析超高精度乘、除法运算规则,提出超高精度整数与普通整数的乘除法运算算法及两个超高精度整数的乘除法运算算法,并分别给出时间复杂度分析及实验数据。给出3个完整的C语言程序,分别完成指定整数高次幂的精确计算并输出、按超高精度要求输出两个普通整数的商、超高精度求圆周率并输出。实验结果证明,在现有的常规软件及语言下,提出的超高精度计算的程序设计方法在工程实践中是简单可行的。     

Forecasting of entropy production due to buoyant convection using support vector machines (SVM) in a partially cooled square cross-sectional room

Forecasting of entropy generation of laminar natural convection in a partially cooled square cross-sectional room has been performed using support vector machines (SVM). The two-dimensional room was modeled as floor heating story with a window. Values of temperature and velocities were obtained by solving governing equations of natural convection with finite difference technique and using these values entropy generation was calculated with its definition. Forecasting of entropy generation due to fluid friction irreversibility (FFI) and heat transfer irreversibility (HTI) were made with known values for unknown parameters using SVM. Thus, calculation time was extremely reduced and values were obtained even for non-convergence cases. It was observed that the SVM was a strong method to predict the entropy generation without computational fluid dynamic analysis for all cases.     

常减压蒸馏过程换热网络的粒子群优化设计方法

提出了1种换热网络的粒子群优化设计方法,提高大规模无分流换热网络的设计速度和效果。采用超结构建立换热网络模型,以各个换热器和换热量为演化个体,以投资和运行费用最小化为优化目标,发挥粒子群的群智特点寻优计算。避免传统设计的计算复杂、不能同时优化投资和运行费用等问题,解决了换热器的换热量受换热条件约束、相互制约而影响设计的难题,保证演化过程中解的可行性,实现设计过程的智能化。以某炼油厂的常减压蒸馏过程的换热网络为实验对象,仿真研究,证明了算法有效和优越。     

Modular Arithmetic for Linear Algebra Computations in the Real Field

The aim of this work is to decrease the bit precision required in computations without affecting the precision of the answer, whether this is computed exactly or within some tolerance. By precision we understand the number of bits in the binary representation of the values involved in the computation, hence a smaller precision requirement leads to a smaller complexity. We achieve this by combining the customary numerical techniques of rounding the least significant bits with the algebraic technique of reduction modulo an integer, which we extend to the reduction modulo a positive number. In particular, we show that, if the sum of several numbers has small magnitude, relative to the magnitude of the summands, then the precision used in the computation of this sum can be decreased without affecting the precision of the answer. Furthermore, if the magnitude of the inner product of two vectors is small and if one of them is filled with “short” binary numbers, then again we may decrease the precision of the computation. The method is applied to the iterative improvement algorithm for a linear system of equations whose coefficients are represented by “short” binary numbers, as well as to the solution of PDEs by means of multigrid methods. Some results of numerical experiments are presented to demonstrate the power of the method.     

Gaussian mixture modeling and learning of neighboring characters for multilingual text extraction in images

This paper proposes an approach based on the statistical modeling and learning of neighboring characters to extract multilingual texts in images. The case of three neighboring characters is represented as the Gaussian mixture model and discriminated from other cases by the corresponding ‘pseudo-probability’ defined under Bayes framework. Based on this modeling, text extraction is completed through labeling each connected component in the binary image as character or non-character according to its neighbors, where a mathematical morphology based method is introduced to detect and connect the separated parts of each character, and a Voronoi partition based method is advised to establish the neighborhoods of connected components. We further present a discriminative training algorithm based on the maximum–minimum similarity (MMS) criterion to estimate the parameters in the proposed text extraction approach. Experimental results in Chinese and English text extraction demonstrate the effectiveness of our approach trained with the MMS algorithm, which achieved the precision rate of 93.56% and the recall rate of 98.55% for the test data set. In the experiments, we also show that the MMS provides significant improvement of overall performance, compared with influential training criterions of the maximum likelihood (ML) and the maximum classification error (MCE).     

Super resolution land cover mapping of hyperspectral images using the deep image prior-based approach

ABSTRACT

Due to the instantaneous field-of-view (IFOV) of the sensor and diversity of land cover types, some pixels, usually named mixed pixels, contain more than one land cover type. Soft classification can predict the portion of each land cover type in mixed pixels in the absence of spatial distribution. The spatial distribution information in mixed pixels can be solved by super resolution mapping (SRM). Typically, SRM involves two steps: soft class value estimation, which is similar to the image super resolution of image restoration, and land cover allocation. A new SRM approach utilizes a deep image prior (DIP) strategy combined with a super resolution convolutional neural network (SRCNN) to estimate fine resolution fraction images for each land cover type; then, a simple and efficient classifier is used to allocate subpixel land cover types under the constraint of the generated fine fraction images. The proposed approach can use prior information of input images to update network parameters and no longer require training data. Experiments on three different cases demonstrate that the subpixel classification accuracy of the proposed DIP-based SRM approach is significantly better than the three conventional SRM approaches and a transfer learning-based neural network SRM approach. In addition, the DIP-SRM approach performs very robustly about small-area objects within multiple land cover types and significantly reduces soft classification uncertainty. The results of this paper provide an extension for utilizing SRCNN to address SRM issues in hyperspectral images.     

哈夫曼编码乘积量化的图像哈希检索方法

目的 基于哈希编码的检索方法是图像检索领域中的经典方法。其原理是将原始空间中相似的图片经哈希函数投影、量化后,在汉明空间中得到相近的哈希码。此类方法一般包括两个过程：投影和量化。投影过程大多采用主成分分析法对原始数据进行降维,但不同方法的量化过程差异较大。对于信息量不均衡的数据,传统的图像哈希检索方法采用等长固定编码位数量化的方式,导致出现低编码效率和低量化精度等问题。为此,本文提出基于哈夫曼编码的乘积量化方法。方法 首先,利用乘积量化法对降维后的数据进行量化,以便较好地保持数据在原始空间中的分布情况。然后,采用子空间方差作为衡量信息量的标准,并以此作为编码位数分配的依据。最后,借助于哈夫曼树,给方差大的子空间分配更多的编码位数。结果 在常用公开数据集MNIST、NUS-WIDE和22K LabelMe上进行实验验证,与原始的乘积量化方法相比,所提出方法能平均降低49%的量化误差,并提高19%的平均准确率。在数据集MNIST上,与同类方法的变换编码方法（TC）进行对比,比较了从32 bit到256 bit编码时的训练时间,本文方法的训练时间能够平均缩短22.5 s。结论 本文提出了一种基于多位编码乘积量化的哈希方法,该方法提高了哈希编码的效率和量化精度,在平均准确率、召回率等性能上优于其他同类算法,可以有效地应用到图像检索相关领域。     

Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites

In this article, a novel CF (collaborative filtering)-based recommender system is developed for e-commerce sites. Unlike the conventional approach in which only binary purchase data are used, the proposed approach analyzes the data captured from the navigational and behavioral patterns of customers, estimates the preference levels of a customer for the products which are clicked but not purchased, and CF is conducted using the preference levels for making recommendations. This also compares with the existing works on clickstream data analysis in which the navigational and behavioral patterns of customers are analyzed for simple relationships with the target variable. The effectiveness of the proposed approach is assessed using an experimental e-commerce site. It is found among other things that the proposed approach outperforms the conventional approach in almost all cases considered. The proposed approach is versatile and can be applied to a variety of e-commerce sites as long as the navigational and behavioral patterns of customers can be captured.     

Real-time validation of vision-based over-height vehicle detection system

Over-height vehicle strikes with low bridges and tunnels are an ongoing problem worldwide. While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we perform a full validation of the system using a constraint-based approach to minimize the number of over-height vehicle misclassifications due to windy conditions. The dataset includes a total of 102 over-height vehicles recorded at frame rates of 25 and 30fps. An analysis is performed of wind and vehicle displacements to track over-height features using optical flow paired with SURF feature detectors. Motion captured within the region of interest was treated as a standard two-class binary linear classification problem with 1 indicating over-height vehicle presence and 0 indicating noise. The algorithm performed with 100% recall, 83.3% precision, false positive rate of 0.2% and warning accuracy of 96.6%.     

一种基于混沌系统及Henon映射的快速图像加密算法

提出一种新的基于混沌系统及Henon映射的图像加密算法．使用经过取模处理的可逆二维非线性Henon映射对各像素点的灰度值进行链式循环迭代,每个迭代环节中Henon映射的参数与迭代次数依次取自一维混沌映射生成的混沌序列．该算法设计简单、运算快速、解密准确,尤其是解决了基于混沌或非线性变换的加密算法中普遍存在的由于计算机的有限精度或各计算机精度不同所导致的解密失败的难题,避免了常用置乱变换算法固有的缺陷及密码强度的不足,具有很高的安全性．     

风云二号气象卫星图像自动几何精校正

介绍了一种风云二号（FY-2）卫星图像自动几何精校正系统和方法。首先获得标称图坐标系下的二值化地标模板,然后通过Bayes后验概率,获得二值化FY-2待匹配图像;通过最大相关系数方法将地标模板与FY-2标称图进行匹配,获得地标偏移量;再通过质量控制获得匹配成功的地标对,实现自动的地标匹配。最后,应用DLT变换对FY-2图像进行几何精校正。大量的测试数据表明,该方法能够有效改进FY-2可见光通道图像定位精度。几何精校正后,可见光通道图像在南北方向上的定位精度提高了31.06%,东西方向上的定位精度提高了45.21%。分析指出FY-2可见光通道图像几何精校正前后,定位精度在星下点地方时正午前后均达到最佳状态,且南北方向上的定位精度优于东西方向上的精度。同时,统计分析结果表明,提出的方法成功率达到95.29%。     

Thermodynamic modeling of the C–Pu system

The thermodynamic modelling of the binary C–Pu system was performed in the framework of the development of a thermodynamic database for nuclear materials, for increasing the knowledge of the physico-chemical behaviour of the fuel and surrounding materials implicated in GFR (gas cooled fast reactor) systems. The critical assessment was carried out using the CALPHAD approach, based on available experimental data on phase diagram and thermodynamic properties of the solid phases.     

Approach for estimating similarity between procedures in differently compiled binaries

ContextDetection of an unauthorized use of a software library is a clone detection problem that in case of commercial products has additional complexity due to the fact that only binary code is available.ObjectiveThe goal of this paper is to propose an approach for estimating the level of similarity between the procedures originating from different binary codes. The assumption is that the clones in the binary codes come from the use of a common software library that may be compiled with different toolsets.MethodThe approach uses a set of software metrics adapted from the high level languages and it also extends the set with new metrics that take into account syntactical changes that are introduced by the usage of different toolsets and optimizations. Moreover, the approach compares metric values and introduces transformers and formulas that can use training data for production of measure of similarities between the two procedures in binary codes. The approach has been evaluated on programs from STAMP benchmark and BusyBox tool, compiled with different toolsets in different modes.ResultsThe experiments with programs from STAMP benchmark show that detecting the same procedures recall can be up to 1.44 times higher using new metrics. Knowledge about the used compiling toolset can bring up to 2.28 times improvement in recall. The experiment with BusyBox tool shows 43% recall for 43% precision.ConclusionThe most useful newly proposed metrics are those that consider the frequency of arithmetic instructions, the number and frequency of occurrences for instructions, and the number of occurrences for target addresses in calls. The best way to combine the results of comparing metrics is to use a geometric mean or when previous knowledge is available, to use an arithmetic mean with appropriate transformer.     

Model selection in binary and tobit quantile regression using the Gibbs sampler

A stochastic search variable selection approach is proposed for Bayesian model selection in binary and tobit quantile regression. A simple and efficient Gibbs sampling algorithm was developed for posterior inference using a location-scale mixture representation of the asymmetric Laplace distribution. The proposed approach is then illustrated via five simulated examples and two real data sets. Results show that the proposed method performs very well under a variety of scenarios, such as the presence of a moderately large number of covariates, collinearity and heterogeneity.     

大功率高精度电致冷温度控制仪的研制

介绍了一种新型的大功率高精度电致冷温控仪。该仪器采用AD590作温度传感器，并用半导体电致冷器ThC加热和冷却被控器件，其结构简单、控温精度高、功耗低、线性好，适用于半导体激光器的温度控制及其它需要恒温工作的部件。     

融合语义—表观特征的无监督前景分割

目的 前景分割是图像理解领域中的重要任务,在无监督条件下,由于不同图像、不同实例往往具有多变的表达形式,这使得基于固定规则、单一类型特征的方法很难保证稳定的分割性能。针对这一问题,本文提出了一种基于语义-表观特征融合的无监督前景分割方法（semantic apparent feature fusion,SAFF）。方法 基于语义特征能够对前景物体关键区域产生精准的响应,但往往产生的前景分割结果只关注于关键区域,缺乏物体的完整表达;而以显著性、边缘为代表的表观特征则提供了更丰富的细节表达信息,但基于表观规则无法应对不同的实例和图像成像模式。为了融合表观特征和语义特征优势,研究建立了融合语义、表观信息的一元区域特征和二元上下文特征编码的方法,实现了对两种特征表达的全面描述。接着,设计了一种图内自适应参数学习的方法,用于计算最适合的特征权重,并生成前景置信分数图。进一步地,使用分割网络来学习不同实例间前景的共性特征。结果 通过融合语义和表观特征并采用图像间共性语义学习的方法,本文方法在PASCAL VOC（pattern analysis,statistical modelling and computational learning visual object classes）2012训练集和验证集上取得了显著超过类别激活映射（class activation mapping,CAM）和判别性区域特征融合方法（discriminative regional feature integration,DRFI）的前景分割性能,在F测度指标上分别提升了3.5%和3.4%。结论 本文方法可以将任意一种语义特征和表观特征前景计算模块作为基础单元,实现对两种策略的融合优化,取得了更优的前景分割性能。     

Jointly Optimized Regressors for Image Super‐resolution

Learning regressors from low‐resolution patches to high‐resolution patches has shown promising results for image super‐resolution. We observe that some regressors are better at dealing with certain cases, and others with different cases. In this paper, we jointly learn a collection of regressors, which collectively yield the smallest super‐resolving error for all training data. After training, each training sample is associated with a label to indicate its ‘best’ regressor, the one yielding the smallest error. During testing, our method bases on the concept of ‘adaptive selection’ to select the most appropriate regressor for each input patch. We assume that similar patches can be super‐resolved by the same regressor and use a fast, approximate kNN approach to transfer the labels of training patches to test patches. The method is conceptually simple and computationally efficient, yet very effective. Experiments on four datasets show that our method outperforms competing methods.     

基于机器视觉和GABP算法的播种检测研究

为了判别育秧播种后每穴超级稻籽粒的数目,基于机器视觉运用了遗传改进的BP神经网络(GABP)算法并结合LabVIEW和Matlab设计了图像采集、处理及结果显示系统.针对采集到的图像,采用迭代阈值法分割并获取二值图像,运用投影法定位秧盘目标检测区域及秧穴,并提取超级稻的面积、周长、形状因子和7个不变矩共10个特征参数,建立基于GABP算法的播种检测模型,分别检测空穴、1粒、2粒、3粒.检测试验结果表明,4种情况的实际检测试验结果与人工检测相对误差分别为3.9％、2.0％、3.74％、5.63％,算法平均处理时间为1.018 s,为进一步实现在线穴播量检测系统设计及精密播种作业提供参考.     

舰载条件下一种新的传递对准方案与仿真研究

传递对准是战术舰载武器惯导系统初始对准的一种有效方法;针对舰载捷联惯导系统的初始对准问题,提出了一种新的动基座快速传递对准方案——速度+角速度匹配方案;给出了这种方法的数学模型,采用一种简单的航行轨迹,在三种典型的海况下,对这一传递对准方法中子惯导姿态误差角,速度误差和舰船甲板变形进行了卡尔曼滤波估计及精度分析;研究结果表明,速度加角速度匹配方法具有稳健的对准精度和快速性,估计精度随海况变化不大。