It is well known that a SMPS （switched-mode power supply） is easy to produce strong EMI （electromagnetic interference） and fails in EMC （electromagnetic compatibility） test for its far field radiation exceeds th...It is well known that a SMPS （switched-mode power supply） is easy to produce strong EMI （electromagnetic interference） and fails in EMC （electromagnetic compatibility） test for its far field radiation exceeds the limits between 30-200 MHz. Based on asymmetry line antenna theory, a novel far field CM （common mode） radiation model, including an equivalent driving source, radiation structure and some key influence factors, is identified and built up for a small flyback power supply. Radiation characteristics of this model are predicted by using Ansoft HFSS software and the model effectiveness is verified by experiment. In the end, the radiation role of some key factors, such as the length of output cable, common mode impedance of AC grid, layout of cable and reflected ground, are studied using simulation in detail.展开更多
Sedimentation is one of the most critical environmental issues facing harbors’authorities that results in significant maintenance and dredging costs.Thus,it is essential to plan and manage the harbors in harmony with...Sedimentation is one of the most critical environmental issues facing harbors’authorities that results in significant maintenance and dredging costs.Thus,it is essential to plan and manage the harbors in harmony with both the environmental and economic aspects to support Integrated Coastal Structures Management(ICSM).Harbors’layout and the permeability of protection structures like breakwaters affect the sediment transport within harbors’basins.Using a multi-step relational research framework,this study aims to design a novel prediction model for estimating the sedimentation quantities in harbors through a comparative approach based on artificial intelligence(AI)algorithms.First,one hundred simulations for different harbor layouts and various breakwater characteristics were numerically performed using a coastal modeling system(CMS)for generating the dataset to train and validate the proposed AIbased models.Second,three AI approaches namely:Support Vector Regression(SVR),Gaussian Process Regression(GPR),and Artificial Neural Networks(ANN)were developed to predict sedimentation quantities.Third,a comparison between the developed models was conducted using quality assessment criteria to evaluate their performance and choose the best one.Fourth,a sensitivity analysis was performed to provide insights into the factors affecting sedimentation.Lastly,a decision support tool was developed to predict harbors’sedimentation quantities.Results showed that the ANN model outperforms other models with mean absolute percentage error(MAPE)equals 4%.Furthermore,sensitivity analysis demonstrated that the main breakwater inclination angle,porosity,and harbor basin width affect significantly sediment transport.This research makes a significant contribution to the management of coastal structures by developing an AI data-driven framework that is beneficial for harbors’authorities.Ultimately,the developed decision-support AI tool could be used to predict harbors’sedimentation quantities in an easy,cheap,accurate,and practical manner compared to physical modeling which is time-consuming and costly.展开更多
Cultural modeling （CM） is an emergent and promising research area in social computing. It aims to develop behavioral models of human groups and analyze the impact of culture factors on human group behavior using com...Cultural modeling （CM） is an emergent and promising research area in social computing. It aims to develop behavioral models of human groups and analyze the impact of culture factors on human group behavior using computational methods. Machine learning methods, in particular classification, play a critical role in such applications. Since various cultural-related data sets possess different characteristics, it is important to gain a computational understanding of performance characteristics of various machine learning methods. In this paper, we investigate the performance of seven representative classification algorithms using a benchmark cultural modeling data set and analyze the experimental results as to group behavior forecasting.展开更多
Devastating tornadoes in China have received growing attention in recent years, but little is known about their formation, structure, and evolution on the tornadic scale. Most of these tornadoes develop within the Eas...Devastating tornadoes in China have received growing attention in recent years, but little is known about their formation, structure, and evolution on the tornadic scale. Most of these tornadoes develop within the East Asian monsoon regime, in an environment quite different from tornadoes in the U.S. In this study, we used an idealized, highresolution（25-m grid spacing） numerical simulation to investigate the deadly EF4（Enhanced Fujita scale category 4）tornado that occurred on 23 June 2016 and claimed 99 lives in Yancheng, Jiangsu Province. A tornadic supercell developed in the simulation that had striking similarities to radar observations. The violent tornado in Funing County was reproduced, exceeding EF4（74 ms–1）, consistent with the on-site damage survey. It was accompanied by a funnel cloud that extended to the surface, and exhibited a double-helix vorticity structure. The signal of tornado genesis was found first at the cloud base in the pressure perturbation field, and then developed both upward and downward in terms of maximum vertical velocity overlapping with the intense vertical vorticity centers. The tornado＇s demise was found to accompany strong downdrafts overlapping with the intense vorticity centers. One of the interesting findings of this work is that a violent surface vortex was able to be generated and maintained, even though the simulation employed a free-slip lower boundary condition. The success of this simulation, despite using an idealized numerical approach, provides a means to investigate more historical tornadoes in China.展开更多
文摘It is well known that a SMPS （switched-mode power supply） is easy to produce strong EMI （electromagnetic interference） and fails in EMC （electromagnetic compatibility） test for its far field radiation exceeds the limits between 30-200 MHz. Based on asymmetry line antenna theory, a novel far field CM （common mode） radiation model, including an equivalent driving source, radiation structure and some key influence factors, is identified and built up for a small flyback power supply. Radiation characteristics of this model are predicted by using Ansoft HFSS software and the model effectiveness is verified by experiment. In the end, the radiation role of some key factors, such as the length of output cable, common mode impedance of AC grid, layout of cable and reflected ground, are studied using simulation in detail.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2021R1A2B5B02002599).
文摘Sedimentation is one of the most critical environmental issues facing harbors’authorities that results in significant maintenance and dredging costs.Thus,it is essential to plan and manage the harbors in harmony with both the environmental and economic aspects to support Integrated Coastal Structures Management(ICSM).Harbors’layout and the permeability of protection structures like breakwaters affect the sediment transport within harbors’basins.Using a multi-step relational research framework,this study aims to design a novel prediction model for estimating the sedimentation quantities in harbors through a comparative approach based on artificial intelligence(AI)algorithms.First,one hundred simulations for different harbor layouts and various breakwater characteristics were numerically performed using a coastal modeling system(CMS)for generating the dataset to train and validate the proposed AIbased models.Second,three AI approaches namely:Support Vector Regression(SVR),Gaussian Process Regression(GPR),and Artificial Neural Networks(ANN)were developed to predict sedimentation quantities.Third,a comparison between the developed models was conducted using quality assessment criteria to evaluate their performance and choose the best one.Fourth,a sensitivity analysis was performed to provide insights into the factors affecting sedimentation.Lastly,a decision support tool was developed to predict harbors’sedimentation quantities.Results showed that the ANN model outperforms other models with mean absolute percentage error(MAPE)equals 4%.Furthermore,sensitivity analysis demonstrated that the main breakwater inclination angle,porosity,and harbor basin width affect significantly sediment transport.This research makes a significant contribution to the management of coastal structures by developing an AI data-driven framework that is beneficial for harbors’authorities.Ultimately,the developed decision-support AI tool could be used to predict harbors’sedimentation quantities in an easy,cheap,accurate,and practical manner compared to physical modeling which is time-consuming and costly.
基金supported in part by the National Natural Science Foundation of China under Grant Nos. 60621001, 60875028,60875049, and 70890084the Ministry of Science and Technology of China under Grant No. 2006AA010106the Chinese Academy of Sciences under Grant Nos. 2F05N01, 2F08N03 and 2F07C01
文摘Cultural modeling （CM） is an emergent and promising research area in social computing. It aims to develop behavioral models of human groups and analyze the impact of culture factors on human group behavior using computational methods. Machine learning methods, in particular classification, play a critical role in such applications. Since various cultural-related data sets possess different characteristics, it is important to gain a computational understanding of performance characteristics of various machine learning methods. In this paper, we investigate the performance of seven representative classification algorithms using a benchmark cultural modeling data set and analyze the experimental results as to group behavior forecasting.
基金Supported by the National Natural Science Foundation of China(41705028,41405095,and 41405006)Basic Research Fund of the Chinese Academy of Meteorological Sciences[2017Y018,2015Z003,and 2017Z017(2017LASW-A02)]
文摘Devastating tornadoes in China have received growing attention in recent years, but little is known about their formation, structure, and evolution on the tornadic scale. Most of these tornadoes develop within the East Asian monsoon regime, in an environment quite different from tornadoes in the U.S. In this study, we used an idealized, highresolution（25-m grid spacing） numerical simulation to investigate the deadly EF4（Enhanced Fujita scale category 4）tornado that occurred on 23 June 2016 and claimed 99 lives in Yancheng, Jiangsu Province. A tornadic supercell developed in the simulation that had striking similarities to radar observations. The violent tornado in Funing County was reproduced, exceeding EF4（74 ms–1）, consistent with the on-site damage survey. It was accompanied by a funnel cloud that extended to the surface, and exhibited a double-helix vorticity structure. The signal of tornado genesis was found first at the cloud base in the pressure perturbation field, and then developed both upward and downward in terms of maximum vertical velocity overlapping with the intense vertical vorticity centers. The tornado＇s demise was found to accompany strong downdrafts overlapping with the intense vorticity centers. One of the interesting findings of this work is that a violent surface vortex was able to be generated and maintained, even though the simulation employed a free-slip lower boundary condition. The success of this simulation, despite using an idealized numerical approach, provides a means to investigate more historical tornadoes in China.
