How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is pro...How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.展开更多
Background and Objective: With the popularity and widespread use of mobile phones, the effects of mobile phone dependence and addiction on individuals’ physical and mental health have attracted more and more attentio...Background and Objective: With the popularity and widespread use of mobile phones, the effects of mobile phone dependence and addiction on individuals’ physical and mental health have attracted more and more attention. The present study aims to analyze the current state of mobile phone addiction and its impact on sleep quality within the population, while also exploring the influence of related factors on sleep quality. Ultimately, this research will provide a scientific foundation for targeted intervention measures and strategies. Methods: A total of 253 permanent residents in Nanjing were randomly selected as study subjects. The Mobile Phone Addiction Index (MPAI) and Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the degree of smartphone addiction and sleep quality of the study subjects. Body mass index (BMI) was measured according to standardized procedures. Independent sample t-test, Chi-square test, rank sum test and multiple linear regression were used to analyze the correlation between mobile phone addiction and sleep quality, and P Results: 117 people (46.2%) were addicted to mobile phones. Chi-square test showed that the rate of mobile phone addiction in drinking group was significantly higher than that in non-drinking group (P P P P P P P P P P Conclusion: Mobile phone addiction may lead to shorter sleep duration and reduce sleep efficiency. The withdrawal of mobile phone addiction may have a negative impact on sleep quality. According to the characteristics of the population, appropriate comprehensive intervention measures should be taken to build an effective evaluation system, so as to reduce the impact of mobile phone addiction and withdrawal problems on sleep and improve sleep quality.展开更多
To remove handwritten texts from an image of a document taken by smart phone,an intelligent removal method was proposed that combines dewarping and Fully Convolutional Network with Atrous Convolutional and Atrous Spat...To remove handwritten texts from an image of a document taken by smart phone,an intelligent removal method was proposed that combines dewarping and Fully Convolutional Network with Atrous Convolutional and Atrous Spatial Pyramid Pooling(FCN-AC-ASPP).For a picture taken by a smart phone,firstly,the image is transformed into a regular image by the dewarping algorithm.Secondly,the FCN-AC-ASPP is used to classify printed texts and handwritten texts.Lastly,handwritten texts can be removed by a simple algorithm.Experiments show that the classification accuracy of the FCN-AC-ASPP is better than FCN,DeeplabV3+,FCN-AC.For handwritten texts removal effect,the method of combining dewarping and FCN-AC-ASPP is superior to FCN-AC-ASP alone.展开更多
随着信息时代的快速发展,使移动智能手机作为方便、快捷的办公方式成为可能。针对目前连锁超市管理需求,利用Windows Phone平台,以Arc GIS Server 9.3和SQL Server 2008作为服务器,设计和开发了桂林市华联连锁超市移动管理系统。系统通...随着信息时代的快速发展,使移动智能手机作为方便、快捷的办公方式成为可能。针对目前连锁超市管理需求,利用Windows Phone平台,以Arc GIS Server 9.3和SQL Server 2008作为服务器,设计和开发了桂林市华联连锁超市移动管理系统。系统通过发布Web Service服务接口和Arc GIS Server,为Windows Phone客户端提供信息查询和数据分析。系统主要是实现了对分店总体信息的查询和分析等功能,如分店分布位置浏览、超市分店信息查询、超市信息空间查询、超市信息时间查询、超市营业状况分析,以及通过分店总体信息的浏览和数据分析快速定位到各分店商品库存管理、销售管理、采购管理业务情况等主要功能。系统的实现使得连锁超市办公场所由固定变为移动,大大改善了便捷性和时效性。展开更多
自杀是当前全球重大公共卫生问题之一,预防自杀已刻不容缓。经过30年的发展,加拿大自杀预防中心(Kids Help Phone)开展的青少年自杀预防教育在加拿大处于领导力地位。KHP下设咨询服务与项目部、收入发展部、财务与行政部、创新科技部、...自杀是当前全球重大公共卫生问题之一,预防自杀已刻不容缓。经过30年的发展,加拿大自杀预防中心(Kids Help Phone)开展的青少年自杀预防教育在加拿大处于领导力地位。KHP下设咨询服务与项目部、收入发展部、财务与行政部、创新科技部、新闻编辑室五个部门;借助这些部门,KHP聘用专业的雇员和志愿者,开发线上和线下实体项目、参与国家战略、加强与其他相关机构的联系,为青少年,尤其是大中小学生提供专业化的自杀预防服务和教育活动。KHP及其开展的青少年自杀预防教育对完善我国自杀预防机构、推进自杀预防教育专业化等,提供了借鉴意义。展开更多
从探讨未来智慧校园移动端GIS产品的角度考量,利用云计算技术,以Microsoft Visual Studio 2010为开发平台,采用C#语言为开发语言,以ArcGIS Server 10为GIS服务端,设计开发了Windows Phone手机移动端的手机校园GIS系统。整个系统主要由...从探讨未来智慧校园移动端GIS产品的角度考量,利用云计算技术,以Microsoft Visual Studio 2010为开发平台,采用C#语言为开发语言,以ArcGIS Server 10为GIS服务端,设计开发了Windows Phone手机移动端的手机校园GIS系统。整个系统主要由校园新闻模块、课程安排模块、地图查询模块、我的日记模块、最近天气模块组成,能够起到使用手机小软件方便安排校园生活的目的,研发过程是对智能校园的一次很有意义的探索和尝试。展开更多
文摘How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.
文摘Background and Objective: With the popularity and widespread use of mobile phones, the effects of mobile phone dependence and addiction on individuals’ physical and mental health have attracted more and more attention. The present study aims to analyze the current state of mobile phone addiction and its impact on sleep quality within the population, while also exploring the influence of related factors on sleep quality. Ultimately, this research will provide a scientific foundation for targeted intervention measures and strategies. Methods: A total of 253 permanent residents in Nanjing were randomly selected as study subjects. The Mobile Phone Addiction Index (MPAI) and Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the degree of smartphone addiction and sleep quality of the study subjects. Body mass index (BMI) was measured according to standardized procedures. Independent sample t-test, Chi-square test, rank sum test and multiple linear regression were used to analyze the correlation between mobile phone addiction and sleep quality, and P Results: 117 people (46.2%) were addicted to mobile phones. Chi-square test showed that the rate of mobile phone addiction in drinking group was significantly higher than that in non-drinking group (P P P P P P P P P P Conclusion: Mobile phone addiction may lead to shorter sleep duration and reduce sleep efficiency. The withdrawal of mobile phone addiction may have a negative impact on sleep quality. According to the characteristics of the population, appropriate comprehensive intervention measures should be taken to build an effective evaluation system, so as to reduce the impact of mobile phone addiction and withdrawal problems on sleep and improve sleep quality.
基金Sponsored by the Scientific Research Project of Zhejiang Provincial Department of Education(Grant No.KYY-ZX-20210329).
文摘To remove handwritten texts from an image of a document taken by smart phone,an intelligent removal method was proposed that combines dewarping and Fully Convolutional Network with Atrous Convolutional and Atrous Spatial Pyramid Pooling(FCN-AC-ASPP).For a picture taken by a smart phone,firstly,the image is transformed into a regular image by the dewarping algorithm.Secondly,the FCN-AC-ASPP is used to classify printed texts and handwritten texts.Lastly,handwritten texts can be removed by a simple algorithm.Experiments show that the classification accuracy of the FCN-AC-ASPP is better than FCN,DeeplabV3+,FCN-AC.For handwritten texts removal effect,the method of combining dewarping and FCN-AC-ASPP is superior to FCN-AC-ASP alone.
文摘自杀是当前全球重大公共卫生问题之一,预防自杀已刻不容缓。经过30年的发展,加拿大自杀预防中心(Kids Help Phone)开展的青少年自杀预防教育在加拿大处于领导力地位。KHP下设咨询服务与项目部、收入发展部、财务与行政部、创新科技部、新闻编辑室五个部门;借助这些部门,KHP聘用专业的雇员和志愿者,开发线上和线下实体项目、参与国家战略、加强与其他相关机构的联系,为青少年,尤其是大中小学生提供专业化的自杀预防服务和教育活动。KHP及其开展的青少年自杀预防教育对完善我国自杀预防机构、推进自杀预防教育专业化等,提供了借鉴意义。
文摘从探讨未来智慧校园移动端GIS产品的角度考量,利用云计算技术,以Microsoft Visual Studio 2010为开发平台,采用C#语言为开发语言,以ArcGIS Server 10为GIS服务端,设计开发了Windows Phone手机移动端的手机校园GIS系统。整个系统主要由校园新闻模块、课程安排模块、地图查询模块、我的日记模块、最近天气模块组成,能够起到使用手机小软件方便安排校园生活的目的,研发过程是对智能校园的一次很有意义的探索和尝试。
