The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features...The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.展开更多
A plant＇s capacity to compensate for pest damage as a function of resource availability needs to be predictable in order to apply biocontrol agents effectively. In this research, it was hypothesized that a weedy plan...A plant＇s capacity to compensate for pest damage as a function of resource availability needs to be predictable in order to apply biocontrol agents effectively. In this research, it was hypothesized that a weedy plant species＇ capacity to compensate for defoliation is related to how resource availability affects a plant＇s growth trajectory. Growth rate trajectory is defined as the percent change in relative growth rate or the slope of a plant＇s relative growth rate. 90 Abutilon theophrasti, a common weed species, in cultivated fields of corn and soybean, grew in a greenhouse for 70 d under three nitrogen （N） fertilization treatments. ＂Unfertilized＂ plants were not fertilized, ＂bulk＂ fertilized plants received 0.6 g N on day 15 and ＂exponential＂ fertilized plants received a total of 0.6 g N supplied at an exponential rate of 10% per day with a starting concentration of 0.02 g N on day 15. On day 25, 15 plants in each N treatment had 75% of total leaf area removed. Biomass and reproductive compensation were determined after 50 d and 70 d of growth. Results showed that bulk plants had the greatest absolute growth, but also the greatest decline in growth rates and the least capacity for compensation. Unfertilized plants had the lowest absolute growth, but declines in growth rates were similar to bulk plants with only a slightly greater compensatory capacity. Exponential plants had intermediate absolute growth, but the least decline in growth rates and the greatest capacity for compensation. This experiment indicates that a plant＇s growth rate trajectory, and not high or low relative growth rates or N availability per se, can be used to predict a weedy plant＇s capacity to compensate for herbivory, and has implications for biocontrol of weedy species.展开更多
This paper presents equations for estimating limiting stand density for Z undulata plantations grown in hot desert areas of Raj asthan State in India. Five different stand level basal area projection models, belonging...This paper presents equations for estimating limiting stand density for Z undulata plantations grown in hot desert areas of Raj asthan State in India. Five different stand level basal area projection models, belonging to the path invariant algebraic difference form of a non-linear growth function, were also tested and compared. These models can be used to predict future basal area as a function of stand variables like dominant height and stem number per hectare and are necessary for reviewing different silvicultural treatment options. Data from 22 sample plots were used for modelling. An all possible growth intervals data structure was used. Both, qualitative and quantitative criteria were used to compare alternative models. The Akaike＇s information criteria differ- ence statistic was used to analyze the predictive ability of the models. Results show that the model proposed by Hui and Gadow performed best and hence this model is recommended for use in predicting basal area development in 12 undulata plantations in the study area. The data used were not from thinned stands, and hence the models may be less accurate when used for predictions when natural mortality is very significant.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.62076126,52075031)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX19_0013)。
文摘The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.
文摘A plant＇s capacity to compensate for pest damage as a function of resource availability needs to be predictable in order to apply biocontrol agents effectively. In this research, it was hypothesized that a weedy plant species＇ capacity to compensate for defoliation is related to how resource availability affects a plant＇s growth trajectory. Growth rate trajectory is defined as the percent change in relative growth rate or the slope of a plant＇s relative growth rate. 90 Abutilon theophrasti, a common weed species, in cultivated fields of corn and soybean, grew in a greenhouse for 70 d under three nitrogen （N） fertilization treatments. ＂Unfertilized＂ plants were not fertilized, ＂bulk＂ fertilized plants received 0.6 g N on day 15 and ＂exponential＂ fertilized plants received a total of 0.6 g N supplied at an exponential rate of 10% per day with a starting concentration of 0.02 g N on day 15. On day 25, 15 plants in each N treatment had 75% of total leaf area removed. Biomass and reproductive compensation were determined after 50 d and 70 d of growth. Results showed that bulk plants had the greatest absolute growth, but also the greatest decline in growth rates and the least capacity for compensation. Unfertilized plants had the lowest absolute growth, but declines in growth rates were similar to bulk plants with only a slightly greater compensatory capacity. Exponential plants had intermediate absolute growth, but the least decline in growth rates and the greatest capacity for compensation. This experiment indicates that a plant＇s growth rate trajectory, and not high or low relative growth rates or N availability per se, can be used to predict a weedy plant＇s capacity to compensate for herbivory, and has implications for biocontrol of weedy species.
基金the State Forest Department,Rajasthan for providing financial support for conducting this study and to their officials for rendering necessary assistance during fieldwork
文摘This paper presents equations for estimating limiting stand density for Z undulata plantations grown in hot desert areas of Raj asthan State in India. Five different stand level basal area projection models, belonging to the path invariant algebraic difference form of a non-linear growth function, were also tested and compared. These models can be used to predict future basal area as a function of stand variables like dominant height and stem number per hectare and are necessary for reviewing different silvicultural treatment options. Data from 22 sample plots were used for modelling. An all possible growth intervals data structure was used. Both, qualitative and quantitative criteria were used to compare alternative models. The Akaike＇s information criteria differ- ence statistic was used to analyze the predictive ability of the models. Results show that the model proposed by Hui and Gadow performed best and hence this model is recommended for use in predicting basal area development in 12 undulata plantations in the study area. The data used were not from thinned stands, and hence the models may be less accurate when used for predictions when natural mortality is very significant.
