Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for rep...Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.展开更多
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
To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized r...To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.展开更多
This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an ...This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an emission entropy from the condense heat recovery system in the air conditioning refrigerating machine were introduced.For the evaluation of the entropies,we developed a new algorithm for the parameter identification,called the composite influence coefficient,based on the Least Squares Support Vector Machine method.By simulation,the numerical experiments shows that the Least Squares Support Vector Machine method is one of the powerful methods for the parameter identification to compute the damage entropy of the condense heat,with the largest training error being-0.025(the relative error being-3.56%),and the biggest test error being 0.015(the relative error being 2.5%).展开更多
Indoor air quality has become an important matter for health and safety. Most manufacturing processes generate aerosols. In the metal cutting industry, dry machining processes are accompanied by dust emission (fogs, f...Indoor air quality has become an important matter for health and safety. Most manufacturing processes generate aerosols. In the metal cutting industry, dry machining processes are accompanied by dust emission (fogs, fine chips and metallic dust in both micrometers and nanometers scales) that has impacts on workers’ health. This research work aimed to understand and reduce the harmful impacts of the machining process on the occupational safety. In this study, an experimental investigation was carried out on fine and ultrafine metallic dust emission during slot milling of 2024-T351, 6061-T6 and 7075-T6 aluminum alloy in dry conditions. It was confirmed that the cutting conditions influence significantly the specific surface area of ultrafine particles. It was also found that the cutting speed is a determinant factor for specific surface area of ultrafine particles and control during the slot milling process.展开更多
The amount of moisture in the air is represented by relative humidity(RH);an ideal level of humidity in the interior environment is between 40%and 60%at temperatures between 18°and 20°Celsius.When the RH fal...The amount of moisture in the air is represented by relative humidity(RH);an ideal level of humidity in the interior environment is between 40%and 60%at temperatures between 18°and 20°Celsius.When the RH falls below this level,the environment becomes dry,which can cause skin dryness,irritation,and discomfort at low temperatures.When the humidity level rises above 60%,a wet atmosphere develops,which encourages the growth of mold and mites.Asthma and allergy symptoms may occur as a result.Human health is harmed by excessive humidity or a lack thereof.Dehumidifiers can be used to provide an optimal level of humidity and a stable and pleasant atmosphere;certain models disinfect and purify the water,reducing the spread of bacteria.The design and implementation of a client-server indoor and outdoor air quality monitoring application are presented in this paper.The Netatmo station was used to acquire the data needed in the application.The client is an Android application that allows the user to monitor air quality over a period of their choosing.For a good monitoring process,the Netatmo modules were used to collect data from both environments(indoor:temperature(T),RH,carbon dioxide(CO_(2)),atmospheric pressure(Pa),noise and outdoor:T and RH).The data is stored in a database,using MySQL.The Android application allows the user to view the evolution of the measured parameters in the form of graphs.Also,the paper presents a prediction model of RH using Azure Machine Learning Studio(Azure ML Studio).The model is evaluated using metrics:Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),Relative Absolute Error(RAE),Relative Squared Error(RSE)and Coefficient of Determination(CoD).展开更多
One of the most important aspects of Bangladesh’s textile industry is denim. Bangladesh now has a new opportunity thanks to the global demand for denim among fashion industry professionals. Entrepreneurs from Banglad...One of the most important aspects of Bangladesh’s textile industry is denim. Bangladesh now has a new opportunity thanks to the global demand for denim among fashion industry professionals. Entrepreneurs from Bangladesh provide denim products to well-known international merchants all over the world. The worldwide denim market is predicted to expand by roughly 8% through the year 2020. We must raise the standard of denim if we are to keep up with the expanding industry. In contrast to projectile and rapier systems, air-jet weaving machines nowadays can weave practically all types of yarns without any issues and at higher rates. Due to this, air-jet looms are an excellent substitute for other weft insertion techniques. This kind of device still has one significant flaw, though, and that is the enormous power consumption brought on by the creation of compressed air. Researchers and manufacturers of air-jet looms have therefore worked very hard to find a solution to this issue and achieve a huge reduction in air consumption without compromising loom performance or fabric quality. Therefore, the purpose of this project is to look into ways to decrease air consumption and reduce auxiliary selvedge waste without any decrease in loom performance and fabric quality on existing air-jet weaving looms which reduce the manufacturing costs with process improvement. Just updating the air pressure allowed a weaving mill to reduce air usage by 11 cfm. So, with just almost no cost, a company with 100 looms could save $0.15 M each year, on compressed air. Two new methods for decreasing process costs on air jet looms have also been developed by this project work.展开更多
文摘Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.
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The National Key Research and Development Plan(No.2019YFB2006402)Talent Introduction Fund Project of Hubei Polytechnic University(No.17xjz01R)Key Scientific Research Project of Hubei Polytechnic University(No.22xjz02A)。
文摘To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.
基金Supported by Program of Science and Technology of Hunan Province(2007FJ2006)Project the Program of Science and Tech-nology of Hunan Province(2007TP4030)Hunan Provincial Natural Science Foundation of China(08JJ3093)
文摘This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an emission entropy from the condense heat recovery system in the air conditioning refrigerating machine were introduced.For the evaluation of the entropies,we developed a new algorithm for the parameter identification,called the composite influence coefficient,based on the Least Squares Support Vector Machine method.By simulation,the numerical experiments shows that the Least Squares Support Vector Machine method is one of the powerful methods for the parameter identification to compute the damage entropy of the condense heat,with the largest training error being-0.025(the relative error being-3.56%),and the biggest test error being 0.015(the relative error being 2.5%).
文摘Indoor air quality has become an important matter for health and safety. Most manufacturing processes generate aerosols. In the metal cutting industry, dry machining processes are accompanied by dust emission (fogs, fine chips and metallic dust in both micrometers and nanometers scales) that has impacts on workers’ health. This research work aimed to understand and reduce the harmful impacts of the machining process on the occupational safety. In this study, an experimental investigation was carried out on fine and ultrafine metallic dust emission during slot milling of 2024-T351, 6061-T6 and 7075-T6 aluminum alloy in dry conditions. It was confirmed that the cutting conditions influence significantly the specific surface area of ultrafine particles. It was also found that the cutting speed is a determinant factor for specific surface area of ultrafine particles and control during the slot milling process.
基金supported by the Project“Entrepreneurial competences and excellence research in doctoral and postdoctoral programs-ANTREDOC”,project cofounded by the European Social Fund financing agreement No.56437/24.07.2019.
文摘The amount of moisture in the air is represented by relative humidity(RH);an ideal level of humidity in the interior environment is between 40%and 60%at temperatures between 18°and 20°Celsius.When the RH falls below this level,the environment becomes dry,which can cause skin dryness,irritation,and discomfort at low temperatures.When the humidity level rises above 60%,a wet atmosphere develops,which encourages the growth of mold and mites.Asthma and allergy symptoms may occur as a result.Human health is harmed by excessive humidity or a lack thereof.Dehumidifiers can be used to provide an optimal level of humidity and a stable and pleasant atmosphere;certain models disinfect and purify the water,reducing the spread of bacteria.The design and implementation of a client-server indoor and outdoor air quality monitoring application are presented in this paper.The Netatmo station was used to acquire the data needed in the application.The client is an Android application that allows the user to monitor air quality over a period of their choosing.For a good monitoring process,the Netatmo modules were used to collect data from both environments(indoor:temperature(T),RH,carbon dioxide(CO_(2)),atmospheric pressure(Pa),noise and outdoor:T and RH).The data is stored in a database,using MySQL.The Android application allows the user to view the evolution of the measured parameters in the form of graphs.Also,the paper presents a prediction model of RH using Azure Machine Learning Studio(Azure ML Studio).The model is evaluated using metrics:Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),Relative Absolute Error(RAE),Relative Squared Error(RSE)and Coefficient of Determination(CoD).
文摘One of the most important aspects of Bangladesh’s textile industry is denim. Bangladesh now has a new opportunity thanks to the global demand for denim among fashion industry professionals. Entrepreneurs from Bangladesh provide denim products to well-known international merchants all over the world. The worldwide denim market is predicted to expand by roughly 8% through the year 2020. We must raise the standard of denim if we are to keep up with the expanding industry. In contrast to projectile and rapier systems, air-jet weaving machines nowadays can weave practically all types of yarns without any issues and at higher rates. Due to this, air-jet looms are an excellent substitute for other weft insertion techniques. This kind of device still has one significant flaw, though, and that is the enormous power consumption brought on by the creation of compressed air. Researchers and manufacturers of air-jet looms have therefore worked very hard to find a solution to this issue and achieve a huge reduction in air consumption without compromising loom performance or fabric quality. Therefore, the purpose of this project is to look into ways to decrease air consumption and reduce auxiliary selvedge waste without any decrease in loom performance and fabric quality on existing air-jet weaving looms which reduce the manufacturing costs with process improvement. Just updating the air pressure allowed a weaving mill to reduce air usage by 11 cfm. So, with just almost no cost, a company with 100 looms could save $0.15 M each year, on compressed air. Two new methods for decreasing process costs on air jet looms have also been developed by this project work.