A review of digital twin in product design and development

In the era of digitalization, there are many emerging technologies, such as the Internet of Things (IoT), Digital Twin (DT), Cloud Computing and Artificial Intelligence (AI), which are quickly developped and used in product design and development. Among those technologies, DT is one promising technology which has been widely used in different industries, especially manufacturing, to monitor the performance, optimize the progresses, simulate the results and predict the potential errors. DT also plays various roles within the whole product lifecycle from design, manufacturing, delivery, use and end-of-life. With the growing demands of individualized products and implementation of Industry 4.0, DT can provide an effective solution for future product design, development and innovation. This paper aims to figure out the current states of DT research focusing on product design and development through summarizing typical industrial cases. Challenges and potential applications of DT in product design and development are also discussed to inspire future studies.     

改进TD3算法在四旋翼无人机避障中的应用

为了提高无人机(Unmanned Aerial Vehicle,UAV)系统的智能避障性能,提出了一种基于双延迟深度确定性策略梯度(Twin Delayed Deep Deterministic Policy Gradient,TD3)的改进算法(Improved Twin Delayed Deep Determini...     

孪生支持向量机综述

孪生支持向量机(Twin Support Vector Machine,TWSVM)是在支持向量机(Support Vector Machine,SVM)的基础上发展而来的一种新的机器学习方法。作为一种二分类的分类器,其基本思想为寻找两个超平面,使得每一个分类面靠近本类样本点而远离另一类样本点。作为一种新兴的机器学习方法,孪生支持向量机自提出以来便引起了国内外学者的广泛关注,已经成为机器学习领域的研究热点。对孪生支持向量机的最新研究进展进行综述,首先介绍了孪生支持向量机的基本概念与基本模型;然后对近几年来新型的孪生支持向量机模型与研究进展进行了总结,并对其代表算法进行了优缺点分析和实验比较;最后对将来的研究工作进行了展望。     

数字孪生丹江口工程水质安全“四预”平台研发及应用

丹江口水库的水质安全关系到南水北调中线供水的保障，数字孪生丹江口是国内首个以水利工程水质安全为业务需求之一的数字孪生工程。针对丹江口水库水体水深大、范围大、污染来源分散和类型多，以及丹库与汉库水动力交换复杂、水质呈现分区分层等特点，研发高效高精度水动力水质机理模型、水质在线动态推演技术、水质浓度场三维仿真技术，首次实现水库全域水质浓度场时空演变全过程的在线推演和三维立体仿真展示，通过海量多源异构数据的科学解析、模型集成与多业务有机融合，实现水质在线推演、预警分析、安全态势预演、预案比选决策的水质安全“四预”功能。水质安全“四预”功能已成功应用于丹江口水质安全管理，为中线水源工程供水水质保障管理提供有力支撑。     

光滑有下界的奖惩结合损失函数的最大间隔双球模型

在极度不平衡分类问题中,球形分类器将分类正确样本的损失计为零,仅使用误分样本构造决策函数.文中提出光滑有下界的奖惩结合损失函数,将分类正确样本的损失计为负,实现对目标函数的奖励,避免边界附近噪声的干扰.基于最大间隔双球面支持向量机,利用损失函数,建立奖惩结合的最大间隔双球模型.通过牛顿法构造两个同心球.小球体在覆盖多数类样本的同时抛弃多余的空隙.大球通过增加两个同心球之间的间隔,排除少数类.实验表明,文中模型分类效果较优.     

Digital Twin Enhanced Dynamic Job-Shop Scheduling

For dynamic scheduling, which is daily decision-making in a job-shop, machine availability prediction, disturbance detection and performance evaluation are always common bottlenecks. Previous research efforts on addressing the bottlenecks primarily emphasize on the analysis of data from the physical job-shop, but with little connection and convergence with its virtual models and simulated data. By introducing digital twin (DT), further convergence between physical and virtual spaces of the job-shop can be achieved, which greatly enables dynamic scheduling. DT fuses both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed. It also enables comprehensive performance evaluation for rescheduling using multiple-dimension models, which can describe geometric properties, physics parameters and behaviors of the machines. In the paper, a five-dimension DT for a machine in the job-shop is introduced first, then the DT-based machine availability prediction, disturbance detection and performance evaluation methods are explored. Based on this, a DT-enhanced dynamic scheduling methodology is proposed. A scheduling process of making hydraulic valves in a machining job-shop is taken as a case study to illustrate the effectiveness and advantages of the proposed method.     

Genetic and environmental influences on problematic Internet use: A twin study

Despite many studies on the prevalence and correlates of problematic Internet use (PIU), we know little about its etiological components. Our main aim is to find out to what extent PIU is influenced by genetic and environmental factors using the classic twin design. A total of 237 Turkish twin-pairs aged 10–25 participated in the study. PIU was measured using the ‘Problematic Internet Use Scale’ (PIUS) developed by Ceyhan, Ceyhan and Gürcan. For male twin-pairs, the monozygotic (MZ) twin correlations were larger than the dizygotic (DZ) twin correlations, indicating that genetic factors influenced scores on the PIUS. However, for female twin-pairs, the MZ correlations were smaller than the DZ correlations for the PIUS, showing that genetic factors did not play a role in female twin-pairs. The influence of both genetic and environmental factors was explored with model-fitting analysis. Results showed that both for the “social comfort/benefit” and “negative consequences associated with the Internet use” sub-dimensions, the best-fitting models were the ADE models whereas both for the “excessive use” sub-dimension and “PIUS-Total”, the best-fittings models were the ACE models. The key result of this study is that the genetic and non-shared environmental effects are equally influential on the overall PIU in male twin-pairs.     

A self-organizing map with twin units capable of describing a nonlinear input-output relation applied to speech code vector mapping

This paper describes a new type of self-organizing map (SOM) with twin units as opposed to the single unit type conventional SOM proposed by Kohonen. The present self-organizing map with twin units (TW-SOM) can describe a nonlinear input-output relation with high accuracy. It is applied to voice conversion problem from bone conduction voice to air conduction voice (nonlinear code vector mapping), and its superiority over the conventional method using Linde-Buzo-Gray (LBG) algorithm is discussed. The tone quality of the converted voice is examined not only from the quantization distortion viewpoint, but also from the auditory sensation viewpoint through actual listening tests. The enhancement of the tone quality was experimentally confirmed.     

一种无约束凸规划多平面修正TWSVM

对支持向量机（Twin Support Vector Machine,TWSVM）的优化思想源于基于广义特征值近似支持向量机（Proximal SVM based on Generalized Eigenvalues,GEPSVM）。该算法将传统SVM问题分解为两个凸规划问题,使得训练速度缩减到原来的1/4。对TWSVM做了修正,基于新的优化准则设计了一种特殊TWSVM（GTWSVM）,在此基础上,提出了快速GTWSVM（FGTWSVM）,其将GTWSVM转换为无约束凸规划问题求解。该算法在保证得到与TWSVM相当的分类性能以及较快的计算速度的同时,还减少了输入空间的特征数以及内存占用。对于非线性问题,FGTWSVM可以减少核函数数目。     

基于马氏距离的孪生多分类支持向量机

孪生支持向量机(TWSVM)的研究是近来机器学习领域的一个热点。TWSVM具有分类精度高、训练速度快等优点,但训练时没有充分利用样本的统计信息。作为TWSVM的改进算法,基于马氏距离的孪生支持向量机(TMSVM)在分类过程中考虑了各类样本的协方差信息,在许多实际问题中有着很好的应用效果。然而TMSVM的训练速度有待提高,并且仅适用于二分类问题。针对这两个问题,将最小二乘思想引入TMSVM,用等式约束取代TMSVM中的不等式约束,将二次规划问题的求解简化为求解两个线性方程组,得到基于马氏距离的最小二乘孪生支持向量机(LSTMSVM),并结合有向无环图策略(DAG)设计出基于马氏距离的最小二乘孪生多分类支持向量机。为了减少DAG结构的误差累积,构造了基于马氏距离的类间可分性度量。人工数据集和UCI数据集上的实验均表明,所提算法不仅有效,而且相对于传统多分类SVM,其分类性能有明显提高。