双峰轧制压力形成因素的数值模拟

在一定条件下的平板轧制过程中,变形区内的轧制压力分布会出现双峰状态。针对文献中的试验条件,采用显式动力学弹性有限元法对平板轧制过程进行数值模拟,模拟结果与试验值基本相符,且轧制压力呈现双峰分布状态。通过对轧制模拟过程中轧件咬入阶段和抛钢阶段的分析,可以得出轧件弹性后外端是引起轧制压力出现峰值的一个原因,接触摩擦力和弹性后外端的共同作用使轧制压力呈现双峰分布状态。     

Empirical analysis of strategy selection for the technology leading and technology catch-up in the IT industry

R&D strategies of companies with low and high technological levels are discussed based on the concept of technology convergence and divergence. However, empirically detecting enterprise technology convergence in the distribution of enterprise technology (total productivity increase) over time and identifying key change factors are challenging. This study used a novel statistical indicator that captures the internal technology distribution change with a single number to clearly measure the technology distribution peak as a change in critical bandwidth for enterprise technology convergence and presented it as evidence of each technology convergence or divergence. Furthermore, this study applied the quantitative technology convergence identification method. Technology convergence appeared from the separation of total corporate productivity distribution of 69 IT companies in Korea in 2019–2020 rather than in 2015–2016. Results indicated that when the total technological level was separated from the technology leading and technology catch-up, IT companies were found to be pursuing R&D strategies for technology catch-up.     

语音特征峰在“仿生耳”中的应用

“仿生耳”对接收到的语言信号的特征峰进行提取，经Ａ／Ｄ转换，编码，解码等处理，对神经性耳聋患者的残留听神经元进行刺激，使其在一定程度上的恢复语音识别能力。     

A Multi-Granularity Density Peak Clustering Algorithm Based on Variational Mode Decomposition

Density peak clustering (DPC) can identify cluster centers quickly,without any prior knowledge.It is supposed that the cluster centers have a high density and large distance.However,some real datasets have a hierarchical structure,which will result in local cluster centers having a high density but a smaller distance.DPC is a flat clustering algorithm that searches for cluster centers globally,without considering local differences.To address this issue,a Multi-granularity DPC (MG-DPC) algorithm based on Variational mode decomposition (VMD) is proposed.MG-DPC can find global cluster centers in the coarse-grained space,as well as local cluster centers in the fine-grained space.In addition,the density is difficult to calculate when the dataset has a high dimension.Neighborhood preserving embedding (NPE) algorithm can maintain the neighborhood relationship between samples while reducing the dimensionality.Moreover,DPC requires human experience in selecting cluster centers.This paper proposes a method for automatically selecting cluster centers based on Chebyshev's inequality.MG-DPC is implemented on the dataset of load-data to realize load classification.The clustering performance is evaluated using five validity indices compared with four typical clustering methods.The experimental results demonstrate that MG-DPC outperforms other comparison methods.     

基于代表点与K近邻的密度峰值聚类算法

密度峰值聚类(density peaks clustering, DPC)是一种基于密度的聚类算法,该算法可以直观地确定类簇数量,识别任意形状的类簇,并且自动检测、排除异常点.然而, DPC仍存在些许不足:一方面, DPC算法仅考虑全局分布,在类簇密度差距较大的数据集聚类效果较差;另一方面, DPC中点的分配策略容易导致“多米诺效应”.为此,基于代表点(representative points)与K近邻(K-nearest neighbors, KNN)提出了RKNN-DPC算法.首先,构造了K近邻密度,再引入代表点刻画样本的全局分布,提出了新的局部密度;然后,利用样本的K近邻信息,提出一种加权的K近邻分配策略以缓解“多米诺效应”;最后,在人工数据集和真实数据集上与5种聚类算法进行了对比实验,实验结果表明,所提出的RKNN-DPC可以更准确地识别类簇中心并且获得更好的聚类结果.