Blind reconstruction of convolutional code based on segmented Walsh-Hadamard transform

Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.

Fine-Grained Classification of Product Images Based on Convolutional Neural Networks

With the rapid development of the Internet of things and e-commerce, feature-based image retrieval and classification have become a serious challenge for shoppers searching websites for relevant product information. The last decade has witnessed great interest in research on content-based feature extraction techniques. Moreover, semantic attributes cannot fully express the rich image information. This paper designs and trains a deep convolutional neural network that the convolution kernel size and the order of network connection are based on the high efficiency of the filter capacity and coverage. To solve the problem of long training time and high resource share of deep convolutional neural network, this paper designed a shallow convolutional neural network to achieve the similar classification accuracy. The deep and shallow convolutional neural networks have data pre-processing, feature extraction and softmax classification. To evaluate the classification performance of the network, experiments were conducted using a public database Caltech256 and a homemade product image database containing 15 species of garment and 5 species of shoes on a total of 20,000 color images from shopping websites. Compared with the classification accuracy of combining content-based feature extraction techniques with traditional support vector machine techniques from 76.3% to 86.2%, the deep convolutional neural network obtains an impressive state-of-the-art classification accuracy of 92.1%, and the shallow convolutional neural network reached a classification accuracy of 90.6%. Moreover, the proposed convolutional neural networks can be integrated and implemented in other colour image database.

Identifying Brand Consistency by Product Differentiation Using CNN

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.

A production prediction method of single well in water flooding oilfield based on integrated temporal convolutional network model

Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.

Blind recognition of k/n rate convolutional encoders from noisy observation

Blind recognition of convolutional codes is not only essential for cognitive radio, but also for non-cooperative context. This paper is dedicated to the blind identification of rate k/n convolutional encoders in a noisy context based on Walsh-Hadamard transformation and block matrix (WHT-BM). The proposed algorithm constructs a system of noisy linear equations and utilizes all its coefficients to recover parity check matrix. It is able to make use of fault-tolerant feature of WHT, thus providing more accurate results and achieving better error performance in high raw bit error rate (BER) regions. Moreover, it is more computationally efficient with the use of the block matrix (BM) method. ? 2017 Beijing Institute of Aerospace Information.

On the Coefficients of Several Classes of Bi-univalent Functions Defined by Convolution

In this paper, we introduce several new subclasses of the function class Σ of bi-univalent functions analytic in the open unit disc defined by convolution.Furthermore, we investigate the bounds of the coefficients |a2| and |a3| for functions in these new subclasses. The results presented in this paper improve or generalize the recent works of other authors.

Matrix Inequalities for the Fan Product and the Hadamard Product of Matrices

A new inequality on the minimum eigenvalue for the Fan product of nonsingular M-matrices is given. In addition, a new inequality on the spectral radius of the Hadamard product of nonnegative matrices is also obtained. These inequalities can improve considerably some previous results.

A new model of the harmonic control based on Hadamard product

In terms of Hadamard product, a new model is proposed for the control of connection coefficients of the state variables of the systems. The control law to stabilize the systems via the regulations of connection coefficients is obtained via a Hadamard product involved bilinear matrix inequalities. This new control model may be of significant applications in many fields, especially may be of some special sense in the emergency control such as isolation and obstruction control.

LIE-TROTTER FORMULA FOR THE HADAMARD PRODUCT

Suppose that A and B are two positive-definite matrices,then,the limit of(A^p/2B^pA^p/2)1/p as p tends to 0 can be obtained by the well known Lie-Trotter formula.In this article,we generalize the usual product of matrices to the Hadamard product denoted as*which is commutative,and obtain the explicit formula of the limit(A^p*B^p)^1/p as p tends to 0.Furthermore,the existence of the limit of(A^p*B^p)^1/p as p tends to+∞is proved.

基于矩阵Hadamard乘积运算的空域冲突检测方法

针对现有的以经纬度为网格剖分的空域表征模型,在高纬度地区网格形变较大,且以空域边界坐标判定空域之间是否重合的冲突检测算法存在的计算速度慢的问题,提出以正二十面体球面菱形离散格网大圆弧剖分为基础,用全等菱形离散格网表征空域,结合空域优先级,利用多层级希尔伯特(Hilbert)空间填充曲线对空域进行统一编码。设计了基于矩阵的空域数字化表征方法,利用哈达玛积(Hadamard)乘积运算快速判定多个空域之间的用空属性是否存在冲突。仿真结果表明:该方法具有较高的网格精度,实现秒级冲突检测,与传统冲突检测算法相比,能够达到降低算法运算量,提高运算速度的目的。

A CONJECTURE CONCERNING THE HADAMARD PRODUCT OF INVERSE M-MATRICES

1 IntroductionFor an n×n matrix A which is an inverse M-matrix,M.Neumann in [1]conjecturedthat the Hadamard product A·A is an inverse of an M-matrix.They have checked hisconjecture without failure on Ultrametric matrices and inverse of MMA-matrices,Uni-pathicmatrices and the Willongby inverse M-matrices.Bo-Ying Wang et al.in[2]haveinvestigated Triangular inverse M-matrices which are closed under the Hadamard multipli-cation.Lu Linzheng,Sun Weiwei and Li Wen in[3]presented a more general

Generalized Inversions of Hadamard and Tensor Products for Matrices

We shall give natural generalized solutions of Hadamard and tensor products equations for matrices by the concept of the Tikhonov regularization combined with the theory of reproducing kernels.

The Exact Solutions of Such Coupled Linear Matrix Fractional Differential Equations of Diagonal Unknown Matrices by Using Hadamard Product

In this paper, we present the general exact solutions of such coupled system of matrix fractional differential equations for diagonal unknown matrices in Caputo sense by using vector extraction operators and Hadamard product. Some illustrated examples are also given to show our new approach.

矩阵Hadamard积的最小特征值新下界

依据Gerschgorin定理,给出非奇异M-矩阵Hadamard积的最小特征值新的下界,新结果只与相关矩阵的元素有关,在计算上比现有结果更容易.新估计式改进了现有文献中的一些结果,是现有结果的有益补充.

A REMARK ON CERTAIN CONVOLUTION OPERATOR

A certain operator D^(a+p-1) defined by convolutions (or Hadamard products) is introduced. The object of this paper is to give an application of the convolution operator D^(a+p-1) to the differential inequalities.

矩阵Hadamard积和乘积的若干范数不等式

文章研究矩阵Hadamard积及普通乘积的酉不变范数与谱范数的不等式。利用分块矩阵的技巧及矩阵特征值的优超理论,得到一系列关于矩阵乘积和Hadamard积的范数不等式,改进Zhan的一个结果,同时推广关于Horn和Mathias的矩阵酉不变范数的Cauchy-Schwarz不等式。

基于卷积神经网络的预制叠合板多目标智能化检测方法

在生产过程中,预制构件尺寸不合格问题将导致其在施工现场无法顺利安装,从而影响工期。为推进预制构件智能化生产的进程,以预制叠合板为例,基于卷积神经网络研究生产过程中的智能检测方法,在生产流水线上设计并安装图像采集系统,建立预制叠合板尺寸检测数据集。通过YOLOv5算法实现对混凝土底板、预埋PVC线盒及外伸钢筋的识别,并以固定磁盒作为基准参照物进行尺寸检测误差分析,实现混凝土底板尺寸、预埋PVC线盒坐标的检测,在降低训练数据集参数规模的工况下保持较高的识别精度。结果表明:该方法可以有效检测预制叠合板的底板数量和尺寸、预埋PVC线盒数量和坐标,并实现弯折方向不合格的外伸钢筋检测,并能降低人工成本,提高检测精度,加快检测速度,提高预制叠合板的出厂质量。

多向加权Tsallis熵最大化导向的自动阈值分割方法

受噪声或随机细节、目标和背景的大小比例、成像时的点扩散等不同因素的影响,许多图像的灰度直方图呈现为无模态、单模态、双模态或者多模态样式.为了在统一框架内处理这4种不同模态情形下的自动阈值选择问题,本文提出了一种多向加权Tsallis熵最大化导向的自动阈值分割方法(Multi-directional Weighted Tsallis Entropy,MWTE).基于新设计的反正切方向性卷积核的多尺度乘积效应,该方法将不同模态的灰度直方图转化为统一的单模态右偏灰度直方图.在4个不同方向上提取出这种特殊的单模态右偏灰度直方图后,通过多向加权策略构建出与原始图像灰度值紧密相关的加权Tsallis熵目标函数,并以该目标函数取最大值时对应的灰度值作为最终分割阈值.本文将提出的方法和3个阈值分割方法、1个软分割方法、1个活动轮廓分割方法以及1个自动聚类分割方法进行了比较.在4种不同模态情形下的4幅合成图像和50幅真实世界图像上的实验结果表明,本文提出的方法虽然在计算效率方面不占有优势,但它对不同模态的测试图像具有更稳健的分割适应性,且在量化分割精度所用的马修斯相关系数方面优于其他6个分割方法.

基于双卷积链的贴标机械手产品中心视觉定位方法

目前对于机械手产品视觉中心的定位主要采用的是被动双目立体视觉定位法,但由于缺乏场景坐标的匹配与预测,导致其视觉定位精度较差。为此,提出基于双卷积链的贴标机械手产品视觉中心定位方法。首先,对相机的内置参数与外置参数进行标定,构建视觉定位场景坐标系。然后,利用卷积神经网络中的双卷积网络结构,对视觉中心场景坐标进行预测。最后,通过匹配像素坐标与图像特征点实现贴标机械手产品中心视觉定位。在实验中,在40 mm×40 mm×40 mm小规模空间场景和180 mm×180 mm×180 mm大规模空间场景中对所提方法进行了定位精度的检验。实验结果表明,采用所提方法进行贴标机械手产品中心视觉定位时,定位方差较小,具备较为理想的定位精度。说明所提方法在机械手产品的定位领域具有较好的应用前景,有助于实现工业自动化生产。

基于动态成本卷积的复杂产品批产路径优化问题建模与求解研究

具有复杂工艺路线的装备产品,其生产系统设计的待决策变量之间通常相互耦合、相互影响,对产品最终的批产成本产生着影响,构成了较为复杂的组合优化问题。对此,提出一种基于动态成本卷积的复杂产品批产路径优化模型。该模型考虑了复杂产品工艺路线的各节点均可能存在多种生产设计选项,在各选项对应着不同生产投入和批量效率的情况下,以最终交付产品的批产成本最小化为目标输出生产系统设计和成本卷积路径。同时,建立了混合整数规划模型,并通过对模型中非线性成分实施可控精度线性化,将模型换化为可最优求解的线性模型。最后,针对某连续生产行业设计了小、中、大规模实验算例,验证了所提模型的可行性、合理性和求解效率。