In this paper,the response properties of galloping energy harvesters under bounded random parameter excitation are studied theoretically.The first-order approximate solution of the galloping energy harvester is derive...In this paper,the response properties of galloping energy harvesters under bounded random parameter excitation are studied theoretically.The first-order approximate solution of the galloping energy harvester is derived by applying the multi-scales method.The expression for the largest Lyapunov exponent that determines the trivial solution is derived,and the corresponding simulation diagrams,including the largest Lyapunov exponent diagrams and time domain diagrams,verify our results.Then the steady-state response moments of the nontrivial solution are studied using the moment method,and the analytical expressions for the first-order and second-order moments of the voltage amplitude are obtained,respectively.The corresponding results show that wind speed enhances the steady-state response moments of the voltage amplitude.Meanwhile,the voltage output can be controlled by adjusting the cubic coefficient.To further verify the response characteristics of the galloping energy harvester,the stationary probability density functions of the displacement and velocity are obtained by the Monte-Carlo simulation method.The results show that the wind speed enhances the displacement of the bluff and the damping ratios should be reduced asmuch as possible to improve the performance.What’smore,the piezoelectric materials also impact the performance of the energy harvester.展开更多
Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecastin...Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.展开更多
In this paper, we present a two species amensalism model with non-monotonic functional response and Allee effect on second species. Local and global stability of the boundary and interior equilibrium are investigated....In this paper, we present a two species amensalism model with non-monotonic functional response and Allee effect on second species. Local and global stability of the boundary and interior equilibrium are investigated. By introducing the Allee effect, we show that the boundary equilibrium have changed from unstable node and saddle into saddle-node. Also, the system subject to an Allee effect has increased the time of reach to its stable steady-state solution, but has no influence on the final density of the two species. Our results are supported by numeric simulations.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12172266,12272283)Young Talent Fund of University Association for Science and Technology in Shaanxi,China(Grant No.20200503)+2 种基金the Bilateral governmental personnel exchange project between China and Slovenia for the years 2021-2023(Grant No.12)Joint University Education Project between China and East European(Grant No.2021122)the Fundamental Research Funds for the Central Universities(Grant No.JB210703).
文摘In this paper,the response properties of galloping energy harvesters under bounded random parameter excitation are studied theoretically.The first-order approximate solution of the galloping energy harvester is derived by applying the multi-scales method.The expression for the largest Lyapunov exponent that determines the trivial solution is derived,and the corresponding simulation diagrams,including the largest Lyapunov exponent diagrams and time domain diagrams,verify our results.Then the steady-state response moments of the nontrivial solution are studied using the moment method,and the analytical expressions for the first-order and second-order moments of the voltage amplitude are obtained,respectively.The corresponding results show that wind speed enhances the steady-state response moments of the voltage amplitude.Meanwhile,the voltage output can be controlled by adjusting the cubic coefficient.To further verify the response characteristics of the galloping energy harvester,the stationary probability density functions of the displacement and velocity are obtained by the Monte-Carlo simulation method.The results show that the wind speed enhances the displacement of the bluff and the damping ratios should be reduced asmuch as possible to improve the performance.What’smore,the piezoelectric materials also impact the performance of the energy harvester.
基金supported by the Collaborative Innovation Center Project of Guangdong Academy of Agricultural Sciences,China(XTXM202202).
文摘Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.
基金supported by the National Natural Science Foundation of China under Grant(11601085)the Natural Science Foundation of Fujian Province(2017J01400)
文摘In this paper, we present a two species amensalism model with non-monotonic functional response and Allee effect on second species. Local and global stability of the boundary and interior equilibrium are investigated. By introducing the Allee effect, we show that the boundary equilibrium have changed from unstable node and saddle into saddle-node. Also, the system subject to an Allee effect has increased the time of reach to its stable steady-state solution, but has no influence on the final density of the two species. Our results are supported by numeric simulations.
