A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation...A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.展开更多
The purpose of this work is to assess wind potential on the Kanfarandé site (Guinea). The data used for this research covers a period of 6 years (2018 to 2023) and consists of in situ data (Boké meteorologic...The purpose of this work is to assess wind potential on the Kanfarandé site (Guinea). The data used for this research covers a period of 6 years (2018 to 2023) and consists of in situ data (Boké meteorological station) and satellite products via NASA Power Larc. The study is based on sorted hourly data (speed and direction). The treatments focus on the monthly, annual and seasonal average of speeds, by sector and their frequencies as well as the annual available powers. The obtained results made it possible, on the one hand, to assess wind potential and, on the other hand, to highlight the most favorable periods for wind energy exploitation. The analyzes show the months of July and August have the best average wind speeds with 5.01 m/s and 5.34 m/s respectively. Average wind speeds are higher during the day than at night with a peak observed at 6 p.m. The study also shows that the prevailing winds are oriented towards the South-West. The Weibull parameters determined for the site give an average of 4.5 m/s for the scale parameter and for the shape parameter 2.40 corresponding to an average power density of 65 w/m2 with an annual available power of 194.80 W/m2 and an annual available energy of 1706.45 kWh/m2.展开更多
Wind erosion represents a formidable environmental challenge and has serious negative impacts on soil health and agricultural productivity, particularly in arid and semi-arid areas. The complex dynamics of wind erosio...Wind erosion represents a formidable environmental challenge and has serious negative impacts on soil health and agricultural productivity, particularly in arid and semi-arid areas. The complex dynamics of wind erosion make its large-scale monitoring and quantification a daunting task. To facilitate the monitoring and quantification of wind erosion, various scientific approaches and methods have been employed. These include sophisticated wind erosion equations and models, wind tunnel experiments, and the application of radionuclides. Additionally, researchers have assessed soil physicochemical properties, used anemometers for wind speed measurement, and deployed dust collectors for particle capture. Remote sensing technologies, wind erosion monitoring stations, and evaluations of wind barriers have also been utilized. Recently, the adoption of machine learning methods has gained popularity. Despite their value, each of these techniques has limitations in capturing the full spectrum of the wind erosion process. This paper examines these limitations and assesses the effectiveness of each method in the context of wind erosion studies. It also outlines directions for future research and suggests pathways that could enhance the understanding and management of wind erosion.展开更多
This paper outlines a plan for the effective reduction of the audible sound level produced by aerodynamic noise from the power-generating turbine blades. The contribution of aerodynamic noise can be divided into two c...This paper outlines a plan for the effective reduction of the audible sound level produced by aerodynamic noise from the power-generating turbine blades. The contribution of aerodynamic noise can be divided into two categories: inflow turbulence and airfoil self-noise. The base model and retrofit blade designs were modeled in SolidWorks. Subsequently, noise prediction simulations were conducted and compared to the base blade model to determine which modification provided the greatest benefit using SolidWorks Flow Simulation. The result of this project is a series of blade retrofit recommendations that produce a more acoustically efficient design and reduce noise complaints while enabling turbines to be placed in locations that require quieter operations.展开更多
Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind s...Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.展开更多
为了提高机载嵌入式系统软件开发效率,快速生成软件原型,同时提升机载嵌入式系统软件的可移植性,研究了基于Wind River Hypervisor的机载嵌入式系统虚拟化技术,对Wind River Hypervisor架构进行了剖析,阐述了hyperkernel的内部结构,指明...为了提高机载嵌入式系统软件开发效率,快速生成软件原型,同时提升机载嵌入式系统软件的可移植性,研究了基于Wind River Hypervisor的机载嵌入式系统虚拟化技术,对Wind River Hypervisor架构进行了剖析,阐述了hyperkernel的内部结构,指明了Wind River Hypervisor中不同类型线程在系统运行过程中所承担的角色以及运行特点,并基于Wind River Hypervisor实现了多种虚拟化架构,通过对不同架构的对比展示了每种架构的特征,同时借助workbench+vxworks653环境证实了在机载嵌入式系统部署Wind River Hypervisor架构的可行性。展开更多
文摘A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.
文摘The purpose of this work is to assess wind potential on the Kanfarandé site (Guinea). The data used for this research covers a period of 6 years (2018 to 2023) and consists of in situ data (Boké meteorological station) and satellite products via NASA Power Larc. The study is based on sorted hourly data (speed and direction). The treatments focus on the monthly, annual and seasonal average of speeds, by sector and their frequencies as well as the annual available powers. The obtained results made it possible, on the one hand, to assess wind potential and, on the other hand, to highlight the most favorable periods for wind energy exploitation. The analyzes show the months of July and August have the best average wind speeds with 5.01 m/s and 5.34 m/s respectively. Average wind speeds are higher during the day than at night with a peak observed at 6 p.m. The study also shows that the prevailing winds are oriented towards the South-West. The Weibull parameters determined for the site give an average of 4.5 m/s for the scale parameter and for the shape parameter 2.40 corresponding to an average power density of 65 w/m2 with an annual available power of 194.80 W/m2 and an annual available energy of 1706.45 kWh/m2.
文摘Wind erosion represents a formidable environmental challenge and has serious negative impacts on soil health and agricultural productivity, particularly in arid and semi-arid areas. The complex dynamics of wind erosion make its large-scale monitoring and quantification a daunting task. To facilitate the monitoring and quantification of wind erosion, various scientific approaches and methods have been employed. These include sophisticated wind erosion equations and models, wind tunnel experiments, and the application of radionuclides. Additionally, researchers have assessed soil physicochemical properties, used anemometers for wind speed measurement, and deployed dust collectors for particle capture. Remote sensing technologies, wind erosion monitoring stations, and evaluations of wind barriers have also been utilized. Recently, the adoption of machine learning methods has gained popularity. Despite their value, each of these techniques has limitations in capturing the full spectrum of the wind erosion process. This paper examines these limitations and assesses the effectiveness of each method in the context of wind erosion studies. It also outlines directions for future research and suggests pathways that could enhance the understanding and management of wind erosion.
文摘This paper outlines a plan for the effective reduction of the audible sound level produced by aerodynamic noise from the power-generating turbine blades. The contribution of aerodynamic noise can be divided into two categories: inflow turbulence and airfoil self-noise. The base model and retrofit blade designs were modeled in SolidWorks. Subsequently, noise prediction simulations were conducted and compared to the base blade model to determine which modification provided the greatest benefit using SolidWorks Flow Simulation. The result of this project is a series of blade retrofit recommendations that produce a more acoustically efficient design and reduce noise complaints while enabling turbines to be placed in locations that require quieter operations.
文摘Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.
文摘为了提高机载嵌入式系统软件开发效率,快速生成软件原型,同时提升机载嵌入式系统软件的可移植性,研究了基于Wind River Hypervisor的机载嵌入式系统虚拟化技术,对Wind River Hypervisor架构进行了剖析,阐述了hyperkernel的内部结构,指明了Wind River Hypervisor中不同类型线程在系统运行过程中所承担的角色以及运行特点,并基于Wind River Hypervisor实现了多种虚拟化架构,通过对不同架构的对比展示了每种架构的特征,同时借助workbench+vxworks653环境证实了在机载嵌入式系统部署Wind River Hypervisor架构的可行性。
