Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biase...Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.展开更多
Supervised topic modeling algorithms have been successfully applied to multi-label document classification tasks.Representative models include labeled latent Dirichlet allocation(L-LDA)and dependency-LDA.However,these...Supervised topic modeling algorithms have been successfully applied to multi-label document classification tasks.Representative models include labeled latent Dirichlet allocation(L-LDA)and dependency-LDA.However,these models neglect the class frequency information of words(i.e.,the number of classes where a word has occurred in the training data),which is significant for classification.To address this,we propose a method,namely the class frequency weight(CF-weight),to weight words by considering the class frequency knowledge.This CF-weight is based on the intuition that a word with higher(lower)class frequency will be less(more)discriminative.In this study,the CF-weight is used to improve L-LDA and dependency-LDA.A number of experiments have been conducted on real-world multi-label datasets.Experimental results demonstrate that CF-weight based algorithms are competitive with the existing supervised topic models.展开更多
E类功率放大器（PA）具有设计简单和高效率的优点,然而频率较高时功率管的寄生输出电容大于E类功率放大器所需的电容,这个寄生输出电容导致E类功率放大器的效率降低。提出一种高频E类功率放大器的设计方法,使用负载牵引得到考虑寄生输...E类功率放大器（PA）具有设计简单和高效率的优点,然而频率较高时功率管的寄生输出电容大于E类功率放大器所需的电容,这个寄生输出电容导致E类功率放大器的效率降低。提出一种高频E类功率放大器的设计方法,使用负载牵引得到考虑寄生输出电容后的最佳负载阻抗,再结合谐波阻抗控制方法设计E类功率放大器。采用飞思卡尔的横向扩散金属氧化物半导体（LDMOS）功率管MRF21010设计了一款工作在930~960 MHz的E类功率放大器。测试数据表明,该功率放大器的输出功率为36.8 d Bm（4.79 W）,具有79.4%的功率附加效率。展开更多
In connection with conversion from energy class KR (KR = log10E R, where ER — seismic energy, J) to the universal magnitude estimation of the Tien Shan crustal earthquakes the development of the self-coordinated corr...In connection with conversion from energy class KR (KR = log10E R, where ER — seismic energy, J) to the universal magnitude estimation of the Tien Shan crustal earthquakes the development of the self-coordinated correlation of the magnitudes (mb , ML, Ms ) and KR with the seismic moment M0 as the base scale became necessary. To this purpose, the first attempt to develop functional correlations in the magnitude—seismic moment system subject to the previous studies has been done. It is assumed that in the expression M (mb , ML , Ms) = Ki + zi log10M0 , the coefficients ki? and zi? are controlled by the parameters of ratio?(where;f0 —corner frequency, Brune, 1970, 1971;M0, N×m). According to the new theoretical predictions common functional correlation of the advanced magnitudes Mm (mbm = mb , MLm = ML , MSm = MS ) from log10M0,? log10t0? and the elastic properties (Ci) can be presented as , where , and , for the averaged elastic properties of the Earth’s crust for thembmthe coefficients Ci= –11.30 and di = 1.0, for MLm: Ci = –14.12, di = 7/6;for MSm : Ci = –16.95 and di = 4/3. For theTien Shan earthquakes (1960-2012 years) it was obtained that , and on the basis of the above expressions we received that MSm = 1.59mbm – 3.06. According to the instrumental data the correlation Ms = 1.57mb – 3.05 was determined. Some other examples of comparison of the calculated and observed magnitude - seismic moment ratios for earthquakes of California, the Kuril Islands, Japan, Sumatra and South America are presented.展开更多
Anomaly detection(AD)is an important aspect of various domains and title insurance(TI)is no exception.Robotic process automation(RPA)is taking over manual tasks in TI business processes,but it has its limitations with...Anomaly detection(AD)is an important aspect of various domains and title insurance(TI)is no exception.Robotic process automation(RPA)is taking over manual tasks in TI business processes,but it has its limitations without the support of artificial intelligence(AI)and machine learning(ML).With increasing data dimensionality and in composite population scenarios,the complexity of detecting anomalies increases and AD in automated document management systems(ADMS)is the least explored domain.Deep learning,being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI.We present a hybrid model for AD,using autoencoders(AE)and a one-class support vector machine(OSVM).In the present study,OSVM receives input features representing real-time documents from the TI business,orchestrated and with dimensions reduced by AE.The results obtained from multiple experiments are comparable with traditional methods and within a business acceptable range,regarding accuracy and performance.展开更多
文摘Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.
基金Project supported by the National Natural Science Foundation of China(No.61602204)
文摘Supervised topic modeling algorithms have been successfully applied to multi-label document classification tasks.Representative models include labeled latent Dirichlet allocation(L-LDA)and dependency-LDA.However,these models neglect the class frequency information of words(i.e.,the number of classes where a word has occurred in the training data),which is significant for classification.To address this,we propose a method,namely the class frequency weight(CF-weight),to weight words by considering the class frequency knowledge.This CF-weight is based on the intuition that a word with higher(lower)class frequency will be less(more)discriminative.In this study,the CF-weight is used to improve L-LDA and dependency-LDA.A number of experiments have been conducted on real-world multi-label datasets.Experimental results demonstrate that CF-weight based algorithms are competitive with the existing supervised topic models.
文摘E类功率放大器（PA）具有设计简单和高效率的优点,然而频率较高时功率管的寄生输出电容大于E类功率放大器所需的电容,这个寄生输出电容导致E类功率放大器的效率降低。提出一种高频E类功率放大器的设计方法,使用负载牵引得到考虑寄生输出电容后的最佳负载阻抗,再结合谐波阻抗控制方法设计E类功率放大器。采用飞思卡尔的横向扩散金属氧化物半导体（LDMOS）功率管MRF21010设计了一款工作在930~960 MHz的E类功率放大器。测试数据表明,该功率放大器的输出功率为36.8 d Bm（4.79 W）,具有79.4%的功率附加效率。
文摘In connection with conversion from energy class KR (KR = log10E R, where ER — seismic energy, J) to the universal magnitude estimation of the Tien Shan crustal earthquakes the development of the self-coordinated correlation of the magnitudes (mb , ML, Ms ) and KR with the seismic moment M0 as the base scale became necessary. To this purpose, the first attempt to develop functional correlations in the magnitude—seismic moment system subject to the previous studies has been done. It is assumed that in the expression M (mb , ML , Ms) = Ki + zi log10M0 , the coefficients ki? and zi? are controlled by the parameters of ratio?(where;f0 —corner frequency, Brune, 1970, 1971;M0, N×m). According to the new theoretical predictions common functional correlation of the advanced magnitudes Mm (mbm = mb , MLm = ML , MSm = MS ) from log10M0,? log10t0? and the elastic properties (Ci) can be presented as , where , and , for the averaged elastic properties of the Earth’s crust for thembmthe coefficients Ci= –11.30 and di = 1.0, for MLm: Ci = –14.12, di = 7/6;for MSm : Ci = –16.95 and di = 4/3. For theTien Shan earthquakes (1960-2012 years) it was obtained that , and on the basis of the above expressions we received that MSm = 1.59mbm – 3.06. According to the instrumental data the correlation Ms = 1.57mb – 3.05 was determined. Some other examples of comparison of the calculated and observed magnitude - seismic moment ratios for earthquakes of California, the Kuril Islands, Japan, Sumatra and South America are presented.
文摘Anomaly detection(AD)is an important aspect of various domains and title insurance(TI)is no exception.Robotic process automation(RPA)is taking over manual tasks in TI business processes,but it has its limitations without the support of artificial intelligence(AI)and machine learning(ML).With increasing data dimensionality and in composite population scenarios,the complexity of detecting anomalies increases and AD in automated document management systems(ADMS)is the least explored domain.Deep learning,being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI.We present a hybrid model for AD,using autoencoders(AE)and a one-class support vector machine(OSVM).In the present study,OSVM receives input features representing real-time documents from the TI business,orchestrated and with dimensions reduced by AE.The results obtained from multiple experiments are comparable with traditional methods and within a business acceptable range,regarding accuracy and performance.
