Climatic changes in the onset of spring in northern China associated with changes in the annual cycle and with a recent warming trend were quantified using a recently developed adaptive data analysis tool, the Ensembl...Climatic changes in the onset of spring in northern China associated with changes in the annual cycle and with a recent warming trend were quantified using a recently developed adaptive data analysis tool, the Ensemble Empirical Mode Decomposition. The study was based on a homogenized daily surface air temperature （SAT） dataset for the period 1955–2003. The annual cycle here is referred to as a refined modulated annual cycle （MAC）. The results show that spring at Beijing has arrived significantly earlier by about 2.98 d （10 yr）-1, of which about 1.85 d （10 yr）-1 is due to changes in the annual cycle and 1.13 d （10 yr）-1 due to the long-term warming trend. Variations in the MAC component explain about 92.5% of the total variance in the Beijing daily SAT series and could cause as much as a 20-day shift in the onset of spring from one year to another. The onset of spring has been advancing all over northern China, but more significant in the east than in the west part of the region. These differences are somehow unexplainable by the zonal pattern of the warming trend over the whole region, but can be explained by opposite changes in the spring phase of the MAC, i.e. advancing in the east while delaying in the west. In the east of northern China, the change in the spring phase of MAC explains 40%–60% of the spring onset trend and is attributable to a weakening Asian winter monsoon. The average sea level pressure in Siberia （55°–80°N, 50°–110°E）, an index of the strength of the winter monsoon, could serve as a potential short-term predictor for the onset of spring in the east of northern China.展开更多
When producing special-shape spring in CNC spring coiler,the setup of the coiler is often a manual work using a trial-and-error method.As a result,the setup of coiler consumes so much time and becomes the bottleneck o...When producing special-shape spring in CNC spring coiler,the setup of the coiler is often a manual work using a trial-and-error method.As a result,the setup of coiler consumes so much time and becomes the bottleneck of the spring production process.In order to cope with this situation,this paper proposes an automatic generation system of setup for CNC spring coiler us- ing case-based reasoning(CBR).The core of the study contains:(1)integrated reasoning model of CBR system;(2)spatial shape describe of special-shape spring based on feature;(3)coiling case representation using shape feature matrix;and(4)case similari- ty measure algorithm.The automatic generation system has implemented with C++Builder 6.0 and is helpful in improving the automaticity and efficiency of spring coiler.展开更多
In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining ...In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.展开更多
基金sponsored by the National Basic Research Program of China(Grant Nos. 2011CB952000, 2006CB400504)the Na-tional Natural Science Foundation of China (Grant No.41005039)+1 种基金Wu was sponsored by the National Science Foundation of USA (ATM-0917743)Yan was sponsored by the National Basic Research Program of China(Grant No. 2009CB421401)
文摘Climatic changes in the onset of spring in northern China associated with changes in the annual cycle and with a recent warming trend were quantified using a recently developed adaptive data analysis tool, the Ensemble Empirical Mode Decomposition. The study was based on a homogenized daily surface air temperature （SAT） dataset for the period 1955–2003. The annual cycle here is referred to as a refined modulated annual cycle （MAC）. The results show that spring at Beijing has arrived significantly earlier by about 2.98 d （10 yr）-1, of which about 1.85 d （10 yr）-1 is due to changes in the annual cycle and 1.13 d （10 yr）-1 due to the long-term warming trend. Variations in the MAC component explain about 92.5% of the total variance in the Beijing daily SAT series and could cause as much as a 20-day shift in the onset of spring from one year to another. The onset of spring has been advancing all over northern China, but more significant in the east than in the west part of the region. These differences are somehow unexplainable by the zonal pattern of the warming trend over the whole region, but can be explained by opposite changes in the spring phase of the MAC, i.e. advancing in the east while delaying in the west. In the east of northern China, the change in the spring phase of MAC explains 40%–60% of the spring onset trend and is attributable to a weakening Asian winter monsoon. The average sea level pressure in Siberia （55°–80°N, 50°–110°E）, an index of the strength of the winter monsoon, could serve as a potential short-term predictor for the onset of spring in the east of northern China.
基金Supported by the Doctoral Programme Foundation of Education Ministry of China under the grant(No.20050699033)
文摘When producing special-shape spring in CNC spring coiler,the setup of the coiler is often a manual work using a trial-and-error method.As a result,the setup of coiler consumes so much time and becomes the bottleneck of the spring production process.In order to cope with this situation,this paper proposes an automatic generation system of setup for CNC spring coiler us- ing case-based reasoning(CBR).The core of the study contains:(1)integrated reasoning model of CBR system;(2)spatial shape describe of special-shape spring based on feature;(3)coiling case representation using shape feature matrix;and(4)case similari- ty measure algorithm.The automatic generation system has implemented with C++Builder 6.0 and is helpful in improving the automaticity and efficiency of spring coiler.
基金This research was funded by the National Natural Science Foundation of China(No.62272124)the National Key Research and Development Program of China(No.2022YFB2701401)+3 种基金Guizhou Province Science and Technology Plan Project(Grant Nos.Qiankehe Paltform Talent[2020]5017)The Research Project of Guizhou University for Talent Introduction(No.[2020]61)the Cultivation Project of Guizhou University(No.[2019]56)the Open Fund of Key Laboratory of Advanced Manufacturing Technology,Ministry of Education(GZUAMT2021KF[01]).
文摘In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.
