Ensuring independent mobility for older adults has become a public health and social concern in China owing to its rapidly aging population.To explore independent mobility trends among older adults and the impact of s...Ensuring independent mobility for older adults has become a public health and social concern in China owing to its rapidly aging population.To explore independent mobility trends among older adults and the impact of sociodemo-graphic characteristics in recent years,this study used data from the Chinese Longitudinal Healthy Longevity Survey from 2012 to 2018,combined with binomial logit regression and CatBoost-Shapley additive explanation(SHAP)method to analyze the relationship between independent mobility and sociodemographic characteristics under bus and walking-oriented environments.Study findings indicated that age and gender significantly affected the independent mobility of older adults.Policymaking should prioritize the needs of older adults,focusing on age and gender differ-ences.Additionally,living expense adequacy significantly influenced independent mobility.Policies should substan-tially support economically disadvantaged older adults,en-suring their basic needs are met through subsidies and other measures.Moreover,the study found a notable impact of widowhood on independent mobility,suggesting enhanced social care and mental health support for widowed older adults,especially those who are long-lived.The outcomes of this study provided evidence for policymakers,which are beneficial for developing elderly-friendly travel policies to ensure and enhance the quality of life and independent mo-bility of older adults.展开更多
地质灾害易发性评价对防灾减灾至关重要,但镇域常因样本稀缺导致评价因子权重难以确定。以陕西省安康市镇坪县城关镇(目标域)为例,提出基于迁移学习的地质灾害易发性预测模型。采用TrAdaBoost算法,将镇坪县全域数据(源域)与城关镇数据结...地质灾害易发性评价对防灾减灾至关重要,但镇域常因样本稀缺导致评价因子权重难以确定。以陕西省安康市镇坪县城关镇(目标域)为例,提出基于迁移学习的地质灾害易发性预测模型。采用TrAdaBoost算法,将镇坪县全域数据(源域)与城关镇数据结合,以CatBoost为基准学习器构建TrAdaBoost-CatBoost模型,实现知识迁移。结果表明:迁移策略显著提升了目标域性能,TrAdaBoost-CatBoost模型受试者工作特征曲线(receiver operating characteristic curve,ROC)下面积(area under the curve,AUC)达0.96,较仅用城关镇数据的CatBoost模型ROC曲线AUC(0.94)提升了0.02;与传统模型对比,TrAdaBoost-CatBoost模型ROC曲线AUC显著优于支持向量机(support vector machine,SVM)模型ROC曲线AUC(0.92)和随机森林(random forest,RF)模型ROC曲线AUC(0.93),分别高出0.04和0.03;迁移框架具普适性,TrAdaBoost-SVM模型ROC曲线AUC为0.94(较SVM模型ROC曲线AUC提升了0.02),TrAdaBoost-RF模型ROC曲线AUC为0.95(较RF模型的AUC提升了0.02),两者性能均得到提升,但TrAdaBoost-CatBoost模型(AUC=0.96)仍保持最优。该模型为小样本区域地质灾害评价提供了高精度解决方案,验证了迁移学习在数据稀缺场景的有效性,对类似区域灾害风险防控具有实际参考意义。展开更多
随着新能源大规模接入电网,发电型燃气轮机常需频繁切换工作状态,导致故障风险上升,因此,异常检测对燃气轮机安全运行更加重要。针对燃气轮机异常检测问题,提出了一种基于NARX-Catboost算法的基线建模方法。采用NARX建立燃气轮机声压特...随着新能源大规模接入电网,发电型燃气轮机常需频繁切换工作状态,导致故障风险上升,因此,异常检测对燃气轮机安全运行更加重要。针对燃气轮机异常检测问题,提出了一种基于NARX-Catboost算法的基线建模方法。采用NARX建立燃气轮机声压特征信号的基线模型,引入CatBoost算法以增强NARX拟合能力,并运用贝叶斯优化对模型超参数进行寻优,最终通过实验数据验证了该融合方法在异常检测方面的有效性。另外,将所提NARX-Catboost与基于向前回归正交最小二乘法的NARX模型(NARX-FROLS)和集成深度随机向量函数链接网络(Ensemble Deep Random Vector Functional Link network, edRVFL)方法及性能进行对比。结果表明:NARX-CatBoost方法对正常声压均方根值的拟合均方根误差(RMSE)值为0.008 50,拟合准确度明显优于NARX-FROLS与edRVFL方法;NARX-CatBoost方法对异常声压均方根的异常检测准确率为96.94%,表明通过正常声压特征数据建立基线模型进行异常检测的可行性与准确性。展开更多
针对现有的电力系统短期负荷预测方法存在预测精度较差的问题,提出一种基于长短期记忆神经网络(Long short term memory,LSTM)和CatBoost组合的短期负荷预测方法,针对电力负荷数据具有时序性和非线性的特点,以及长短期记忆网络不能直接...针对现有的电力系统短期负荷预测方法存在预测精度较差的问题,提出一种基于长短期记忆神经网络(Long short term memory,LSTM)和CatBoost组合的短期负荷预测方法,针对电力负荷数据具有时序性和非线性的特点,以及长短期记忆网络不能直接处理类别型特征,对处理后的电力负荷数据建立LSTM负荷预测模型和CatBoost负荷预测模型;用方差倒数法确定加权系数,得到LSTM和CatBoost组合模型的预测值;最后使用实际负荷数据对算法有效性进行验证,预测结果表明采用LSTM和CatBoost组合模型的方法在负荷预测精度上有显著的提高。展开更多
基金The National Natural Science Foundation of China (No. 52272367)the Natural Science Foundation of Jiangsu Province (No. BK20231324)。
文摘Ensuring independent mobility for older adults has become a public health and social concern in China owing to its rapidly aging population.To explore independent mobility trends among older adults and the impact of sociodemo-graphic characteristics in recent years,this study used data from the Chinese Longitudinal Healthy Longevity Survey from 2012 to 2018,combined with binomial logit regression and CatBoost-Shapley additive explanation(SHAP)method to analyze the relationship between independent mobility and sociodemographic characteristics under bus and walking-oriented environments.Study findings indicated that age and gender significantly affected the independent mobility of older adults.Policymaking should prioritize the needs of older adults,focusing on age and gender differ-ences.Additionally,living expense adequacy significantly influenced independent mobility.Policies should substan-tially support economically disadvantaged older adults,en-suring their basic needs are met through subsidies and other measures.Moreover,the study found a notable impact of widowhood on independent mobility,suggesting enhanced social care and mental health support for widowed older adults,especially those who are long-lived.The outcomes of this study provided evidence for policymakers,which are beneficial for developing elderly-friendly travel policies to ensure and enhance the quality of life and independent mo-bility of older adults.
文摘地质灾害易发性评价对防灾减灾至关重要,但镇域常因样本稀缺导致评价因子权重难以确定。以陕西省安康市镇坪县城关镇(目标域)为例,提出基于迁移学习的地质灾害易发性预测模型。采用TrAdaBoost算法,将镇坪县全域数据(源域)与城关镇数据结合,以CatBoost为基准学习器构建TrAdaBoost-CatBoost模型,实现知识迁移。结果表明:迁移策略显著提升了目标域性能,TrAdaBoost-CatBoost模型受试者工作特征曲线(receiver operating characteristic curve,ROC)下面积(area under the curve,AUC)达0.96,较仅用城关镇数据的CatBoost模型ROC曲线AUC(0.94)提升了0.02;与传统模型对比,TrAdaBoost-CatBoost模型ROC曲线AUC显著优于支持向量机(support vector machine,SVM)模型ROC曲线AUC(0.92)和随机森林(random forest,RF)模型ROC曲线AUC(0.93),分别高出0.04和0.03;迁移框架具普适性,TrAdaBoost-SVM模型ROC曲线AUC为0.94(较SVM模型ROC曲线AUC提升了0.02),TrAdaBoost-RF模型ROC曲线AUC为0.95(较RF模型的AUC提升了0.02),两者性能均得到提升,但TrAdaBoost-CatBoost模型(AUC=0.96)仍保持最优。该模型为小样本区域地质灾害评价提供了高精度解决方案,验证了迁移学习在数据稀缺场景的有效性,对类似区域灾害风险防控具有实际参考意义。
文摘随着新能源大规模接入电网,发电型燃气轮机常需频繁切换工作状态,导致故障风险上升,因此,异常检测对燃气轮机安全运行更加重要。针对燃气轮机异常检测问题,提出了一种基于NARX-Catboost算法的基线建模方法。采用NARX建立燃气轮机声压特征信号的基线模型,引入CatBoost算法以增强NARX拟合能力,并运用贝叶斯优化对模型超参数进行寻优,最终通过实验数据验证了该融合方法在异常检测方面的有效性。另外,将所提NARX-Catboost与基于向前回归正交最小二乘法的NARX模型(NARX-FROLS)和集成深度随机向量函数链接网络(Ensemble Deep Random Vector Functional Link network, edRVFL)方法及性能进行对比。结果表明:NARX-CatBoost方法对正常声压均方根值的拟合均方根误差(RMSE)值为0.008 50,拟合准确度明显优于NARX-FROLS与edRVFL方法;NARX-CatBoost方法对异常声压均方根的异常检测准确率为96.94%,表明通过正常声压特征数据建立基线模型进行异常检测的可行性与准确性。
文摘针对现有的电力系统短期负荷预测方法存在预测精度较差的问题,提出一种基于长短期记忆神经网络(Long short term memory,LSTM)和CatBoost组合的短期负荷预测方法,针对电力负荷数据具有时序性和非线性的特点,以及长短期记忆网络不能直接处理类别型特征,对处理后的电力负荷数据建立LSTM负荷预测模型和CatBoost负荷预测模型;用方差倒数法确定加权系数,得到LSTM和CatBoost组合模型的预测值;最后使用实际负荷数据对算法有效性进行验证,预测结果表明采用LSTM和CatBoost组合模型的方法在负荷预测精度上有显著的提高。
