为揭示纺织产品生命周期评价研究进展,以中国知网(CNKI)数据库和Web of Science核心数据库中纺织产品生命周期评价文献为数据源,对文献的发文量、发文作者和载文期刊等进行统计,并对文献中的核算产品、核算边界、核算数据和结果评价等...为揭示纺织产品生命周期评价研究进展,以中国知网(CNKI)数据库和Web of Science核心数据库中纺织产品生命周期评价文献为数据源,对文献的发文量、发文作者和载文期刊等进行统计,并对文献中的核算产品、核算边界、核算数据和结果评价等进行探讨。研究发现:2002-2020年纺织产品生命周期评价的研究文献数量整体呈上升趋势;中文文献中,东华大学的核心作者数量、发文量以及文献被引次数最多,英文文献核心作者集中于欧洲;《纺织导报》和Journal of Cleaner Production分别是中文和英文文献载文量最多的期刊;文献集中于天然纤维尤其是棉纤维类纺织产品的环境影响评价,对化学纤维类纺织产品的生命周期评价研究相对较少;纺织产品生命周期评价研究没有统一的核算边界和高质量的区域化数据库,评价指标体系尚未统一化和标准化。展开更多
This paper presents an effective clustering mode and a novel clustering result evaluating mode. Clustering mode has two limited integral parameters. Evaluating mode evaluates clustering results and gives each a mark. ...This paper presents an effective clustering mode and a novel clustering result evaluating mode. Clustering mode has two limited integral parameters. Evaluating mode evaluates clustering results and gives each a mark. The higher mark the clustering result gains, the higher quality it has. By organizing two modes in different ways, we can build two clustering algorithms： SECDU（Self-Expanded Clustering Algorithm based on Density Units） and SECDUF（Self-Expanded Clustering Algorithm Based on Density Units with Evaluation Feedback Section）. SECDU enumerates all value pairs of two parameters of clustering mode to process data set repeatedly and evaluates every clustering result by evaluating mode. Then SECDU output the clustering result that has the highest evaluating mark among all the ones. By applying ＂hill-climbing algorithm＂, SECDUF improves clustering efficiency greatly. Data sets that have different distribution features can be well adapted to both algorithms. SECDU and SECDUF can output high-quality clustering results. SECDUF tunes parameters of clustering mode automatically and no man＇s action involves through the whole process. In addition, SECDUF has a high clustering performance.展开更多
文摘为揭示纺织产品生命周期评价研究进展,以中国知网(CNKI)数据库和Web of Science核心数据库中纺织产品生命周期评价文献为数据源,对文献的发文量、发文作者和载文期刊等进行统计,并对文献中的核算产品、核算边界、核算数据和结果评价等进行探讨。研究发现:2002-2020年纺织产品生命周期评价的研究文献数量整体呈上升趋势;中文文献中,东华大学的核心作者数量、发文量以及文献被引次数最多,英文文献核心作者集中于欧洲;《纺织导报》和Journal of Cleaner Production分别是中文和英文文献载文量最多的期刊;文献集中于天然纤维尤其是棉纤维类纺织产品的环境影响评价,对化学纤维类纺织产品的生命周期评价研究相对较少;纺织产品生命周期评价研究没有统一的核算边界和高质量的区域化数据库,评价指标体系尚未统一化和标准化。
基金Supported by the National Natural Science Foundation of China(60573089)
文摘This paper presents an effective clustering mode and a novel clustering result evaluating mode. Clustering mode has two limited integral parameters. Evaluating mode evaluates clustering results and gives each a mark. The higher mark the clustering result gains, the higher quality it has. By organizing two modes in different ways, we can build two clustering algorithms： SECDU（Self-Expanded Clustering Algorithm based on Density Units） and SECDUF（Self-Expanded Clustering Algorithm Based on Density Units with Evaluation Feedback Section）. SECDU enumerates all value pairs of two parameters of clustering mode to process data set repeatedly and evaluates every clustering result by evaluating mode. Then SECDU output the clustering result that has the highest evaluating mark among all the ones. By applying ＂hill-climbing algorithm＂, SECDUF improves clustering efficiency greatly. Data sets that have different distribution features can be well adapted to both algorithms. SECDU and SECDUF can output high-quality clustering results. SECDUF tunes parameters of clustering mode automatically and no man＇s action involves through the whole process. In addition, SECDUF has a high clustering performance.
