JDA软件公司联手全球领先的品牌策划公司Lippincott近日对外公布了JDA最新的品牌徽标,其中包含了全新的公司品牌标语：＂Plan to deliver TM＂。在当今快速变化的零售和制造市场环境下,与最出色的解决方案提供商展开合作、获得持续的竞...JDA软件公司联手全球领先的品牌策划公司Lippincott近日对外公布了JDA最新的品牌徽标,其中包含了全新的公司品牌标语：＂Plan to deliver TM＂。在当今快速变化的零售和制造市场环境下,与最出色的解决方案提供商展开合作、获得持续的竞争优势变得愈发重要。基于这一市场现状,JDA联手Lippincott设计了全新徽标,彰显JDA的市场领导地位。展开更多
JDA软件集团公司近日宣布,全球最大的眼镜制造商陆逊梯卡集团（Luxottica Group Sp A）选择实施的JDA供应链计划方案正式上线,帮助提升其全球范围的生产与产能管理能力。JDA解决方案将与陆逊梯卡现有的ERP系统进行集成,帮助该公司提升...JDA软件集团公司近日宣布,全球最大的眼镜制造商陆逊梯卡集团（Luxottica Group Sp A）选择实施的JDA供应链计划方案正式上线,帮助提升其全球范围的生产与产能管理能力。JDA解决方案将与陆逊梯卡现有的ERP系统进行集成,帮助该公司提升供应链响应性,满足快速变化的产品组合的高度季节性和离散化需求。陆逊梯卡集团是全球领先的高档、奢侈及运动型眼镜的设计、制造和经销商。展开更多
近日,JDA软件集团公司宣布,全球最大的眼镜制造商陆逊梯卡集团（Luxottica Group SpA）选择实施的JDA供应链计划方案正式上线,帮助提升其全球范围的生产与产能管理能力。JDA解决方案将与陆逊梯卡现有的ERP系统进行集成,帮助该公司提升...近日,JDA软件集团公司宣布,全球最大的眼镜制造商陆逊梯卡集团（Luxottica Group SpA）选择实施的JDA供应链计划方案正式上线,帮助提升其全球范围的生产与产能管理能力。JDA解决方案将与陆逊梯卡现有的ERP系统进行集成,帮助该公司提升供应链响应性,满足快速变化的产品组合的高度季节性和离散化需求。陆逊梯卡集团是全球领先的高档、奢侈及运动型眼镜的高度季节性和离散化需求。展开更多
Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize ...Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains.For each new domain,it is required to collect and annotate a large amount of data,and the training of the algorithm does not benefit from prior knowledge,leading to redundant calculation workload and excessive time investment.To address this problem,the paper proposes a method that could transfer gesture data in different domains.We use a red-green-blue(RGB)Camera to collect images of the gestures,and use Leap Motion to collect the coordinates of 21 joint points of the human hand.Then,we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning.This paper compares the effects of three classification algorithms,i.e.,support vector machine(SVM),broad learning system(BLS)and deep learning(DL).We also compare learning performances with and without using the joint distribution adaptation(JDA)algorithm.The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion.In addition,we found that when using DL to classify the data,excessive training on the source domain may reduce the accuracy of recognition in the target domain.展开更多
文摘JDA软件公司联手全球领先的品牌策划公司Lippincott近日对外公布了JDA最新的品牌徽标,其中包含了全新的公司品牌标语：＂Plan to deliver TM＂。在当今快速变化的零售和制造市场环境下,与最出色的解决方案提供商展开合作、获得持续的竞争优势变得愈发重要。基于这一市场现状,JDA联手Lippincott设计了全新徽标,彰显JDA的市场领导地位。
文摘JDA软件集团公司近日宣布,全球最大的眼镜制造商陆逊梯卡集团（Luxottica Group Sp A）选择实施的JDA供应链计划方案正式上线,帮助提升其全球范围的生产与产能管理能力。JDA解决方案将与陆逊梯卡现有的ERP系统进行集成,帮助该公司提升供应链响应性,满足快速变化的产品组合的高度季节性和离散化需求。陆逊梯卡集团是全球领先的高档、奢侈及运动型眼镜的设计、制造和经销商。
文摘近日,JDA软件集团公司宣布,全球最大的眼镜制造商陆逊梯卡集团（Luxottica Group SpA）选择实施的JDA供应链计划方案正式上线,帮助提升其全球范围的生产与产能管理能力。JDA解决方案将与陆逊梯卡现有的ERP系统进行集成,帮助该公司提升供应链响应性,满足快速变化的产品组合的高度季节性和离散化需求。陆逊梯卡集团是全球领先的高档、奢侈及运动型眼镜的高度季节性和离散化需求。
基金supported by National Nature Science Foundation of China(NSFC)(Nos.U20A20200,61811530281,and 61861136009)Guangdong Regional Joint Foundation(No.2019B1515120076)+1 种基金Fundamental Research for the Central Universitiesin part by the Foshan Science and Technology Innovation Team Special Project(No.2018IT100322)。
文摘Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains.For each new domain,it is required to collect and annotate a large amount of data,and the training of the algorithm does not benefit from prior knowledge,leading to redundant calculation workload and excessive time investment.To address this problem,the paper proposes a method that could transfer gesture data in different domains.We use a red-green-blue(RGB)Camera to collect images of the gestures,and use Leap Motion to collect the coordinates of 21 joint points of the human hand.Then,we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning.This paper compares the effects of three classification algorithms,i.e.,support vector machine(SVM),broad learning system(BLS)and deep learning(DL).We also compare learning performances with and without using the joint distribution adaptation(JDA)algorithm.The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion.In addition,we found that when using DL to classify the data,excessive training on the source domain may reduce the accuracy of recognition in the target domain.
