Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241122
Yifan Xia
The study aims to provide a way for impaired people to learn music better through music visualization. We established a relationship between piano key frequencies and Chladni patterns. Firstly, we performed modal analysis simulations using ANSYS, covering the full range of excitable modes in the frequency range of the piano keys. However, since the natural frequencies of the simulation results were slightly different from experimental results, we used the hammering method to validate the simulation results and to demonstrate that the finite element modal analysis was able to simulate the dynamic properties of the circular plate well. Subsequent studies have found that different excitation frequencies in the lower frequency range may also affect the generation of Chladni patterns. Using harmonic analysis in ANSYS, we confirmed that tones of similar frequency on the same plate can produce different Chladni patterns, thus greatly simplifying the previous problem of having to use multiple plates of different shapes and sizes in order to visualize most of the piano keys. Based on the graphical simulation of a single tone, we have successfully extended it to a melody. This result provides strong support for effectively helping the hearing impaired to learn to play musical instruments, makes it possible to present music in a visual form, and opens up new possibilities for the wider use of music education tools in the hearing-impaired community.
{"title":"Seeing the piano sound Exploration on utilizing finite element analysis to visualize piano sound","authors":"Yifan Xia","doi":"10.54254/2753-8818/30/20241122","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241122","url":null,"abstract":"The study aims to provide a way for impaired people to learn music better through music visualization. We established a relationship between piano key frequencies and Chladni patterns. Firstly, we performed modal analysis simulations using ANSYS, covering the full range of excitable modes in the frequency range of the piano keys. However, since the natural frequencies of the simulation results were slightly different from experimental results, we used the hammering method to validate the simulation results and to demonstrate that the finite element modal analysis was able to simulate the dynamic properties of the circular plate well. Subsequent studies have found that different excitation frequencies in the lower frequency range may also affect the generation of Chladni patterns. Using harmonic analysis in ANSYS, we confirmed that tones of similar frequency on the same plate can produce different Chladni patterns, thus greatly simplifying the previous problem of having to use multiple plates of different shapes and sizes in order to visualize most of the piano keys. Based on the graphical simulation of a single tone, we have successfully extended it to a melody. This result provides strong support for effectively helping the hearing impaired to learn to play musical instruments, makes it possible to present music in a visual form, and opens up new possibilities for the wider use of music education tools in the hearing-impaired community.","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 62","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139621160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241084
Jiachen Liu
Two-dimensional quantum materials are currently a hot research area in the field of materials. These unique materials allow electrons to move freely in only two dimensions and the other dimension is limited within the atom scale. The isolation of Graphene in 2004 showed the advantages of two-dimensional (2D) materials and led a rapid development in this field. Meanwhile, it stimulates the synthesis and research of the materials beyond carbon. 2D materials beyond carbon have different components and structures, showing a broader range of remarkable properties and applications than traditional graphene. This article will pay attention to the phenomenon and mechanism of these exceptional properties, including superconductivity, ferromagnetism, antiferromagnetism, and quantum spin liquid phase. Additionally, potential applications and future prospects of 2D materials beyond carbon will be explored. With the progress of technology, 2D materials beyond carbon are expected to have exciting developments in various fields, leading to significant changes in human life and production
{"title":"Properties and applications of two-dimensional quantum materials beyond carbon","authors":"Jiachen Liu","doi":"10.54254/2753-8818/30/20241084","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241084","url":null,"abstract":"Two-dimensional quantum materials are currently a hot research area in the field of materials. These unique materials allow electrons to move freely in only two dimensions and the other dimension is limited within the atom scale. The isolation of Graphene in 2004 showed the advantages of two-dimensional (2D) materials and led a rapid development in this field. Meanwhile, it stimulates the synthesis and research of the materials beyond carbon. 2D materials beyond carbon have different components and structures, showing a broader range of remarkable properties and applications than traditional graphene. This article will pay attention to the phenomenon and mechanism of these exceptional properties, including superconductivity, ferromagnetism, antiferromagnetism, and quantum spin liquid phase. Additionally, potential applications and future prospects of 2D materials beyond carbon will be explored. With the progress of technology, 2D materials beyond carbon are expected to have exciting developments in various fields, leading to significant changes in human life and production","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139621060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241098
Qiyu Fan
The Twin Paradox is a representative problem in special relativity. It proposed a problem that from a spacecraft that is moving at a speed close to light, the earths time will be slower; in contrast, the earth will also consider the time of the spacecraft slower. However, this paradoxs solution can expand beyond the special relativity and Lorentz transformation. Basically, the seeming paradox can be solved using the period when the spacecraft turns around. Therefore, it can be considered a good way to have a better understanding of relativity. In this paper, this paradox is explained in three ways: the Lorentz transformation, the Minkowski geometry, a special geometry including both space and time, which is proposed to have a better explanation of the special relativity and gravitational time dilation in general relativity, and will also expand to other effects in relativity, such as the gravitational redshift. This paper hopes to offer some references for a better understanding of the Twin Paradox.
{"title":"A review of the explanations to the twin paradox","authors":"Qiyu Fan","doi":"10.54254/2753-8818/30/20241098","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241098","url":null,"abstract":"The Twin Paradox is a representative problem in special relativity. It proposed a problem that from a spacecraft that is moving at a speed close to light, the earths time will be slower; in contrast, the earth will also consider the time of the spacecraft slower. However, this paradoxs solution can expand beyond the special relativity and Lorentz transformation. Basically, the seeming paradox can be solved using the period when the spacecraft turns around. Therefore, it can be considered a good way to have a better understanding of relativity. In this paper, this paradox is explained in three ways: the Lorentz transformation, the Minkowski geometry, a special geometry including both space and time, which is proposed to have a better explanation of the special relativity and gravitational time dilation in general relativity, and will also expand to other effects in relativity, such as the gravitational redshift. This paper hopes to offer some references for a better understanding of the Twin Paradox.","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139621136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241029
Tianqing Bei, Hanlei Gao, Ruiyang Gao, Guangtong Shi
Immigration is a very important link in the current international society. This paper will study and predict the net immigration of the United States and the Central African Republic through two different models- drift model and ARIMA model, and to further explore the trends and influencing factors of migration between these countries. The results show that from 1960 to 2021, net migration from the United States and the Central African Republic showed very different trends. The United States, as a developed country, attracts a large number of immigrants from all over the world, while the Central African Republic, as a developing country, the flow of immigrants is mainly affected by economic, political and social factors in the region. Therefore, it can be seen that developing countries and developed countries have different impacts on the number of immigrants. This study provides a basis for further understanding of population migration and net migration of United States and Central African Republic.
{"title":"Statistical forecasting of U.S. and Central African Republic net migration","authors":"Tianqing Bei, Hanlei Gao, Ruiyang Gao, Guangtong Shi","doi":"10.54254/2753-8818/30/20241029","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241029","url":null,"abstract":"Immigration is a very important link in the current international society. This paper will study and predict the net immigration of the United States and the Central African Republic through two different models- drift model and ARIMA model, and to further explore the trends and influencing factors of migration between these countries. The results show that from 1960 to 2021, net migration from the United States and the Central African Republic showed very different trends. The United States, as a developed country, attracts a large number of immigrants from all over the world, while the Central African Republic, as a developing country, the flow of immigrants is mainly affected by economic, political and social factors in the region. Therefore, it can be seen that developing countries and developed countries have different impacts on the number of immigrants. This study provides a basis for further understanding of population migration and net migration of United States and Central African Republic.","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 95","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139621286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241129
Xintian Zou
Although previous studies have given a better prediction model for the Americans unemployment rate, due to the short time and different time nodes, the parameters of the model and the seasonality and the stability of the time series are also different. In this study, the ARIMA model, which is the most widely used in the time series, is adopted and the seasonal influence is added to the model according to the selected time period. At the same time, two models are used to predict the unemployment rate in the United States from January 2017 to January 2019. The stability of the model was determined by Dickey-Fuller test, and the fitting and prediction effects of the two models were compared by comparing the values of AIC and MSE. With the fitting prediction method of the unemployment rate in the United States, this paper can analyze and predict the unemployment rate in other Western countries, and can further compare and analyze the reasons with China s unemployment rate, which is convenient for us to better regulate macroeconomic policies.
{"title":"U.S. unemployment rate prediction using time series model","authors":"Xintian Zou","doi":"10.54254/2753-8818/30/20241129","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241129","url":null,"abstract":"Although previous studies have given a better prediction model for the Americans unemployment rate, due to the short time and different time nodes, the parameters of the model and the seasonality and the stability of the time series are also different. In this study, the ARIMA model, which is the most widely used in the time series, is adopted and the seasonal influence is added to the model according to the selected time period. At the same time, two models are used to predict the unemployment rate in the United States from January 2017 to January 2019. The stability of the model was determined by Dickey-Fuller test, and the fitting and prediction effects of the two models were compared by comparing the values of AIC and MSE. With the fitting prediction method of the unemployment rate in the United States, this paper can analyze and predict the unemployment rate in other Western countries, and can further compare and analyze the reasons with China s unemployment rate, which is convenient for us to better regulate macroeconomic policies.","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139621816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241095
Shiqi Liu
Gear and belts/chains are the most common solution for modern mechanical devices. The allowable stress and gear ratio are two of the main factors that decide the performance of the ear section for transmission. The allowable stress can be calculated by bending stress and contact stress from design criteria. The performance of the belt solution is decided by the belt/chain material, center distance and the belt length. The allowable strength of the belt/chain solution is strongly related to the materials selected. With the current study of belts and gears, the related factors could be calculated to design the optimum shape and material. Dual clutch transmission (DCT) and continuously variable transmission (CVT) are two widely applied transmissions with gear and belt/chain solutions that are introduced in the research. The advantages of DCT could be the reliability and high transmission efficiency of the gear solution and the space and energy efficiency of belt/chain structure advantages of the CVT. Both DCT and CVT could be applied in different conditions to maximize the specific performance.
{"title":"Common transmission theory and wide application","authors":"Shiqi Liu","doi":"10.54254/2753-8818/30/20241095","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241095","url":null,"abstract":"Gear and belts/chains are the most common solution for modern mechanical devices. The allowable stress and gear ratio are two of the main factors that decide the performance of the ear section for transmission. The allowable stress can be calculated by bending stress and contact stress from design criteria. The performance of the belt solution is decided by the belt/chain material, center distance and the belt length. The allowable strength of the belt/chain solution is strongly related to the materials selected. With the current study of belts and gears, the related factors could be calculated to design the optimum shape and material. Dual clutch transmission (DCT) and continuously variable transmission (CVT) are two widely applied transmissions with gear and belt/chain solutions that are introduced in the research. The advantages of DCT could be the reliability and high transmission efficiency of the gear solution and the space and energy efficiency of belt/chain structure advantages of the CVT. Both DCT and CVT could be applied in different conditions to maximize the specific performance.","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139621602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241062
Yijie Zhang
Predicting stock prices has long been a subject of keen interest due to its financial implications and inherent complexity. The examination of existing literature suggests the need for a focused study encompassing a diverse spectrum of stocks within a specific sector. In this research, the author evaluates the efficacy of the AutoRegressive Integrated Moving Average (ARIMA) model in forecasting Googles stock performance. The data used in this paper comes from the Chinese corn market price of 2018 to October 2023. The selection of the ARIMA model is based on its widespread acceptance and straightforward nature. This paper also explores how the accuracy of predictions is influenced by various historical data points. Simultaneously, the projections indicate that Googles stock is poised for continued growth in the upcoming weeks. This investigation aims to provide valuable insights into the stock markets behaviour, particularly within the context of Google, by leveraging the ARIMA models capabilities.
{"title":"The comprehensive analysis of Googles stock using ARIMA model","authors":"Yijie Zhang","doi":"10.54254/2753-8818/30/20241062","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241062","url":null,"abstract":"Predicting stock prices has long been a subject of keen interest due to its financial implications and inherent complexity. The examination of existing literature suggests the need for a focused study encompassing a diverse spectrum of stocks within a specific sector. In this research, the author evaluates the efficacy of the AutoRegressive Integrated Moving Average (ARIMA) model in forecasting Googles stock performance. The data used in this paper comes from the Chinese corn market price of 2018 to October 2023. The selection of the ARIMA model is based on its widespread acceptance and straightforward nature. This paper also explores how the accuracy of predictions is influenced by various historical data points. Simultaneously, the projections indicate that Googles stock is poised for continued growth in the upcoming weeks. This investigation aims to provide valuable insights into the stock markets behaviour, particularly within the context of Google, by leveraging the ARIMA models capabilities.","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139622936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241086
Renjun Huang
Against the backdrop of increasing attention to the integration of machine learning and stock analysis, stock prediction models are a hot topic. The question this paper is studying in this study is which stock prediction model is more accurate in predicting stocks. The method of this study is based on the stock prices of new energy vehicle leader Tesla Motors in the past three years as a data set, using a random forest model and an SVR model to predict the stock prices over the next 10 days. Based on the parameter MSE values of the training models of two stock prediction models, compare their sizes to determine the accuracy and stability of the models. This study found that the stock prediction results of the SVR model are more accurate and stable than those of the random forest model. Therefore, it is believed that the stock prediction model using the SVR method will have more market value and occupy an important position in the integration of machine learning and stock trading analysis.
{"title":"Research on the selection of stock prediction models","authors":"Renjun Huang","doi":"10.54254/2753-8818/30/20241086","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241086","url":null,"abstract":"Against the backdrop of increasing attention to the integration of machine learning and stock analysis, stock prediction models are a hot topic. The question this paper is studying in this study is which stock prediction model is more accurate in predicting stocks. The method of this study is based on the stock prices of new energy vehicle leader Tesla Motors in the past three years as a data set, using a random forest model and an SVR model to predict the stock prices over the next 10 days. Based on the parameter MSE values of the training models of two stock prediction models, compare their sizes to determine the accuracy and stability of the models. This study found that the stock prediction results of the SVR model are more accurate and stable than those of the random forest model. Therefore, it is believed that the stock prediction model using the SVR method will have more market value and occupy an important position in the integration of machine learning and stock trading analysis.","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139623056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241074
Tianhong Gao
Most recently, the technology of unmanned vehicles/systems (UVs/USs) has experienced substantial growth. These vehicles can operate on land, in water as well as and even through the air. They have become increasingly important in various civil applications, incorporating surveillance, precise farming, imagery collection, and search and rescue operations, surpassing manned systems in many aspects. Increased mission safety and cheaper operating expenses are provided by unmanned vehicles. UAVs, often known as unmanned aerial vehicles, are one of them that are widely utilized in construction projects because of its benefits including low costs for upkeep, simple deployment, the capacity to hover, and outstanding mobility. The most significant challenge facing the application of drones is their endurance, especially in harsh and windy weather conditions where drones consume power at a faster rate. In this paper, this work explores improvements in drone endurance through lightweight material design, battery enhancements, and path planning by studying and organizing relevant literature from various authors. These advancements aim to effectively extend the flight time of drones, thereby enabling them to successfully complete missions.
{"title":"Improving the flight endurance of multi-rotor drones in windy days","authors":"Tianhong Gao","doi":"10.54254/2753-8818/30/20241074","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241074","url":null,"abstract":"Most recently, the technology of unmanned vehicles/systems (UVs/USs) has experienced substantial growth. These vehicles can operate on land, in water as well as and even through the air. They have become increasingly important in various civil applications, incorporating surveillance, precise farming, imagery collection, and search and rescue operations, surpassing manned systems in many aspects. Increased mission safety and cheaper operating expenses are provided by unmanned vehicles. UAVs, often known as unmanned aerial vehicles, are one of them that are widely utilized in construction projects because of its benefits including low costs for upkeep, simple deployment, the capacity to hover, and outstanding mobility. The most significant challenge facing the application of drones is their endurance, especially in harsh and windy weather conditions where drones consume power at a faster rate. In this paper, this work explores improvements in drone endurance through lightweight material design, battery enhancements, and path planning by studying and organizing relevant literature from various authors. These advancements aim to effectively extend the flight time of drones, thereby enabling them to successfully complete missions.","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139621422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.54254/2753-8818/30/20241078
Xinyu Qian
Bike sharing has become a much more popular topic nowadays. Not only do the producers in bike-sharing need to provide a relatively accurate number of bikes in each period, but also the consumers need to have a general understanding of the number of bikes in each hour. This study analyses the dataset of bike-sharing rentals in 2011 in Washington, D.C. using machine learning, after training, testing, analyzing, and visualizing the dataset, the author chose the best model-random forest to predict it through the method of cross-test. The research result shows that the number of rentals in bike-sharing is the highest in the morning and evening travel peaks in one day, the highest in working days in one week, and the highest in autumn in one year. This information can help the bike-sharing service to prepare different quantities of bike-sharing at different times, and the customers would have a better overview of the bike demand when they plan to rent one. The whole research process provides valuable information for the service providers and users of bike-sharing.
{"title":"Using machine learning for bike sharing demand prediction","authors":"Xinyu Qian","doi":"10.54254/2753-8818/30/20241078","DOIUrl":"https://doi.org/10.54254/2753-8818/30/20241078","url":null,"abstract":"Bike sharing has become a much more popular topic nowadays. Not only do the producers in bike-sharing need to provide a relatively accurate number of bikes in each period, but also the consumers need to have a general understanding of the number of bikes in each hour. This study analyses the dataset of bike-sharing rentals in 2011 in Washington, D.C. using machine learning, after training, testing, analyzing, and visualizing the dataset, the author chose the best model-random forest to predict it through the method of cross-test. The research result shows that the number of rentals in bike-sharing is the highest in the morning and evening travel peaks in one day, the highest in working days in one week, and the highest in autumn in one year. This information can help the bike-sharing service to prepare different quantities of bike-sharing at different times, and the customers would have a better overview of the bike demand when they plan to rent one. The whole research process provides valuable information for the service providers and users of bike-sharing.","PeriodicalId":489336,"journal":{"name":"Theoretical and Natural Science","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139622881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：