Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects an...Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.展开更多
Complex survey designs often involve unequal selection probabilities of clus-ters or units within clusters. When estimating models for complex survey data, scaled weights are incorporated into the likelihood, producin...Complex survey designs often involve unequal selection probabilities of clus-ters or units within clusters. When estimating models for complex survey data, scaled weights are incorporated into the likelihood, producing a pseudo likeli-hood. In a 3-level weighted analysis for a binary outcome, we implemented two methods for scaling the sampling weights in the National Health Survey of Pa-kistan (NHSP). For NHSP with health care utilization as a binary outcome we found age, gender, household (HH) goods, urban/rural status, community de-velopment index, province and marital status as significant predictors of health care utilization (p-value < 0.05). The variance of the random intercepts using scaling method 1 is estimated as 0.0961 (standard error 0.0339) for PSU level, and 0.2726 (standard error 0.0995) for household level respectively. Both esti-mates are significantly different from zero (p-value < 0.05) and indicate consid-erable heterogeneity in health care utilization with respect to households and PSUs. The results of the NHSP data analysis showed that all three analyses, weighted (two scaling methods) and un-weighted, converged to almost identical results with few exceptions. This may have occurred because of the large num-ber of 3rd and 2nd level clusters and relatively small ICC. We performed a sim-ulation study to assess the effect of varying prevalence and intra-class correla-tion coefficients (ICCs) on bias of fixed effect parameters and variance components of a multilevel pseudo maximum likelihood (weighted) analysis. The simulation results showed that the performance of the scaled weighted estimators is satisfactory for both scaling methods. Incorporating simulation into the analysis of complex multilevel surveys allows the integrity of the results to be tested and is recommended as good practice.展开更多
滑坡负样本在基于统计模型的滑坡危险度制图中具有重要作用,能够抑制模型的高估,以合理区划滑坡危险区与非危险区。目标空间外向化采样法(Target Space Exteriorization Sampling,TSES)是一种代表性的基于环境特征空间的负样本采样方法...滑坡负样本在基于统计模型的滑坡危险度制图中具有重要作用,能够抑制模型的高估,以合理区划滑坡危险区与非危险区。目标空间外向化采样法(Target Space Exteriorization Sampling,TSES)是一种代表性的基于环境特征空间的负样本采样方法,以往研究表明,TSES在基于广义加性模型的滑坡危险度制图中的应用效果较好,但是其采集的负样本是"虚拟"的样本,只存在于环境特征空间中,无法映射到地理空间,因而无法通过野外检核验证所采集负样本的可靠性。针对这一问题,该文提出一种改进TSES方法,不仅可以在环境特征空间中进行负样本采样,而且使得采集的负样本可以映射到地理空间中。以甘肃省油房沟流域为研究区,在TSES与改进TSES两种负样本采样方法下分别对油房沟流域构建基于支持向量机(Support Vector Machine,SVM)的滑坡危险度推测模型,对比并分析两种负样本采样方法下的滑坡危险度制图精度。结果发现,改进TSES方法采集的负样本在基于SVM的滑坡危险度制图中应用效果比TSES好,表明改进的TSES是一种有效的负样本采样方法。展开更多
服役载荷模拟试验能准确地预测零部件的疲劳寿命。为节省试验时间,急需开发出一套合理、实用的加速试验方法。重点研究在不具备结构局部应变响应,仅具备外部激励载荷如力、位移、和加速度等情况下的试验加速方法。基于修正Miner准则,以...服役载荷模拟试验能准确地预测零部件的疲劳寿命。为节省试验时间,急需开发出一套合理、实用的加速试验方法。重点研究在不具备结构局部应变响应,仅具备外部激励载荷如力、位移、和加速度等情况下的试验加速方法。基于修正Miner准则,以伪损伤保留比例作为小载荷删除准则,并结合疲劳数据编辑(Fatigue data editing,FDE)技术,提出一套便于工程应用的服役载荷模拟试验加速方法。以某轿车前副车架的疲劳试验为例,分别编制伪损伤保留比例为99%、95%和90%的加速谱。综合考虑各加速谱的载荷特征和加速效果,选用95%加速谱、90%加速谱和原始谱分别建立台架试验。试验结果表明两种加速谱在有效节省试验时间的同时,均获得了与原始谱相同的试验结果,且90%加速谱的加速试验效果更为显著。本方法便于工程应用,可为其他汽车零部件的服役载荷模拟试验提供参考。展开更多
文摘Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.
文摘Complex survey designs often involve unequal selection probabilities of clus-ters or units within clusters. When estimating models for complex survey data, scaled weights are incorporated into the likelihood, producing a pseudo likeli-hood. In a 3-level weighted analysis for a binary outcome, we implemented two methods for scaling the sampling weights in the National Health Survey of Pa-kistan (NHSP). For NHSP with health care utilization as a binary outcome we found age, gender, household (HH) goods, urban/rural status, community de-velopment index, province and marital status as significant predictors of health care utilization (p-value < 0.05). The variance of the random intercepts using scaling method 1 is estimated as 0.0961 (standard error 0.0339) for PSU level, and 0.2726 (standard error 0.0995) for household level respectively. Both esti-mates are significantly different from zero (p-value < 0.05) and indicate consid-erable heterogeneity in health care utilization with respect to households and PSUs. The results of the NHSP data analysis showed that all three analyses, weighted (two scaling methods) and un-weighted, converged to almost identical results with few exceptions. This may have occurred because of the large num-ber of 3rd and 2nd level clusters and relatively small ICC. We performed a sim-ulation study to assess the effect of varying prevalence and intra-class correla-tion coefficients (ICCs) on bias of fixed effect parameters and variance components of a multilevel pseudo maximum likelihood (weighted) analysis. The simulation results showed that the performance of the scaled weighted estimators is satisfactory for both scaling methods. Incorporating simulation into the analysis of complex multilevel surveys allows the integrity of the results to be tested and is recommended as good practice.
文摘服役载荷模拟试验能准确地预测零部件的疲劳寿命。为节省试验时间,急需开发出一套合理、实用的加速试验方法。重点研究在不具备结构局部应变响应,仅具备外部激励载荷如力、位移、和加速度等情况下的试验加速方法。基于修正Miner准则,以伪损伤保留比例作为小载荷删除准则,并结合疲劳数据编辑(Fatigue data editing,FDE)技术,提出一套便于工程应用的服役载荷模拟试验加速方法。以某轿车前副车架的疲劳试验为例,分别编制伪损伤保留比例为99%、95%和90%的加速谱。综合考虑各加速谱的载荷特征和加速效果,选用95%加速谱、90%加速谱和原始谱分别建立台架试验。试验结果表明两种加速谱在有效节省试验时间的同时,均获得了与原始谱相同的试验结果,且90%加速谱的加速试验效果更为显著。本方法便于工程应用,可为其他汽车零部件的服役载荷模拟试验提供参考。
