Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often proh...Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.展开更多
Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism learning.In order to m...Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism learning.In order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.展开更多
In this paper;we analyze the performance of a broadcast packet in a VANET with the slotted ALOHA protocol where locations of vehicles are modeled by a one-dimensional Poisson point process. We consider the packet deli...In this paper;we analyze the performance of a broadcast packet in a VANET with the slotted ALOHA protocol where locations of vehicles are modeled by a one-dimensional Poisson point process. We consider the packet delivery probability under a broadcast delay constraint. Since the successful transmission of a broadcast packet is significantly affected by interferences at receivers which are spatially correlated,让 is important to capture the spatial correlations properly in order to obtain an accurate expression of the packet delivery probability in a VANET. However, the exact analysis of the spatial correlations in interference is not mathematically tractable. In this paper we provide an accurate approximation of the spatial correlations in interference and derive the packet delivery probability with the help of the approximation. Numerical and simulation results are provided to validate our analysis and to investigate the performance of a VANET.展开更多
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)Program(IITP-2020-0-01816)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)This research was also supported by National Research Foundation(NRF)of Korea Grant funded by the Korean Government(MSIT)(No.2021R1A4A3022102).
文摘Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.
基金supported in part by Shenzhen Science and Technology Program(ZDSYS2021102111141502)the Shenzhen Institute of Artificial Intelligence and Robotics for Society+3 种基金the National Natural Science Foundation of China(62277001)the Scientific Research Program of Beijing Municipal Education Commission(KZ202110011017)the National Key Technology R&D Program of China(SQ2020YFB10027)Major Science and Technology Special Project of Yunnan Province(202102AD080006)。
文摘Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism learning.In order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.
文摘In this paper;we analyze the performance of a broadcast packet in a VANET with the slotted ALOHA protocol where locations of vehicles are modeled by a one-dimensional Poisson point process. We consider the packet delivery probability under a broadcast delay constraint. Since the successful transmission of a broadcast packet is significantly affected by interferences at receivers which are spatially correlated,让 is important to capture the spatial correlations properly in order to obtain an accurate expression of the packet delivery probability in a VANET. However, the exact analysis of the spatial correlations in interference is not mathematically tractable. In this paper we provide an accurate approximation of the spatial correlations in interference and derive the packet delivery probability with the help of the approximation. Numerical and simulation results are provided to validate our analysis and to investigate the performance of a VANET.
