This study was carried out to examine the development of an “elderly tele-nursing model” for care provided in-home by family members and through remote nursing systems in a super-aging society. This model studied th...This study was carried out to examine the development of an “elderly tele-nursing model” for care provided in-home by family members and through remote nursing systems in a super-aging society. This model studied the travel time, cost, and means of transportation of care providers. The pre-survey results regarding elderly tele-nursing show that a son/daughter can visit a parent more than once a week. In the results, the time required for elderly tele-nursing was influenced by whether or not the visitor uses the shinkansen （bullet train of Japan）. In the main survey, based on 40 questionnaires, clear differences were observed according to whether visits were “every two weeks” or “once per month”. Furthermore, this result was also indicated by t-tests.展开更多
Ambiguous expression is a common phenomenon in facial expression recognition(FER).Because of the existence of ambiguous expression,the effect of FER is severely limited.The reason maybe that the single label of the da...Ambiguous expression is a common phenomenon in facial expression recognition(FER).Because of the existence of ambiguous expression,the effect of FER is severely limited.The reason maybe that the single label of the data cannot effectively describe complex emotional intentions which are vital in FER.Label distribution learning contains more information and is a possible way to solve this problem.To apply label distribution learning on FER,a label distribution expression recognition algorithm based on asymptotic truth value is proposed.Under the premise of not incorporating extraneous quantitative information,the original information of database is fully used to complete the generation and utilization of label distribution.Firstly,in training part,single label learning is used to collect the mean value of the overall distribution of data.Then,the true value of data label is approached gradually on the granularity of data batch.Finally,the whole network model is retrained using the generated label distribution data.Experimental results show that this method can improve the accuracy of the network model obviously,and has certain competitiveness compared with the advanced algorithms.展开更多
文摘This study was carried out to examine the development of an “elderly tele-nursing model” for care provided in-home by family members and through remote nursing systems in a super-aging society. This model studied the travel time, cost, and means of transportation of care providers. The pre-survey results regarding elderly tele-nursing show that a son/daughter can visit a parent more than once a week. In the results, the time required for elderly tele-nursing was influenced by whether or not the visitor uses the shinkansen （bullet train of Japan）. In the main survey, based on 40 questionnaires, clear differences were observed according to whether visits were “every two weeks” or “once per month”. Furthermore, this result was also indicated by t-tests.
基金National Youth Natural Science Foundation of China(No.61806006)Innovation Program for Graduate of Jiangsu Province(No.KYLX160-781)Project Supported by Jiangsu University Superior Discipline Construction Project。
文摘Ambiguous expression is a common phenomenon in facial expression recognition(FER).Because of the existence of ambiguous expression,the effect of FER is severely limited.The reason maybe that the single label of the data cannot effectively describe complex emotional intentions which are vital in FER.Label distribution learning contains more information and is a possible way to solve this problem.To apply label distribution learning on FER,a label distribution expression recognition algorithm based on asymptotic truth value is proposed.Under the premise of not incorporating extraneous quantitative information,the original information of database is fully used to complete the generation and utilization of label distribution.Firstly,in training part,single label learning is used to collect the mean value of the overall distribution of data.Then,the true value of data label is approached gradually on the granularity of data batch.Finally,the whole network model is retrained using the generated label distribution data.Experimental results show that this method can improve the accuracy of the network model obviously,and has certain competitiveness compared with the advanced algorithms.
