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Population dynamics and considerations for the conservation of the rare Cycas fairylakea in China 被引量:1
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作者 WANG Dian-pei PENG Shao-lin +1 位作者 CHEN Fei-peng JI Shu-yi 《Forestry Studies in China》 CAS 2012年第2期118-123,共6页
Quantitative dynamics and viability of a rare and wild Cycas fairylakea population were studied with a time-specific life table, a Leslie matrix model and a survival function in order to provide scientific guidance fo... Quantitative dynamics and viability of a rare and wild Cycas fairylakea population were studied with a time-specific life table, a Leslie matrix model and a survival function in order to provide scientific guidance for its protection. The results of the time- specific life table show that this C. fairylakea population suffered a high death rate in three age classes, i.e., age class 1 (0-15 years), V (61-75 years) and VI (76-90 years). The Leslie matrix model suggests that the number of plants would decline from the present 1613 to 59 per hectare in 150 years. Furthermore, the viability analysis indicates that seedlings have the highest mortality density rate and that middle-aged plants (i.e., 61-75 years, 76-90 years) have high mortality density rate and hazard rate. These conditions affect natural regeneration of the population and lead to a lack of seedlings which in turn causes the extinction of the population. An in situ conservation of the population should be established and protection measures taken as soon as possible. 展开更多
关键词 Cycasfairylakea population rare species time-specific life table Leslie matrix model VIABILITY
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Online learning-based model predictive trajectory control for connected and autonomous vehicles: Modeling and physical tests
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作者 Qianwen Li Peng Zhang +2 位作者 Handong Yao Zhiwei Chen Xiaopeng Li 《Journal of Intelligent and Connected Vehicles》 EI 2024年第2期86-96,共11页
Motivated by the promising benefits of connected and autonomous vehicles (CAVs) in improving fuelefficiency, mitigating congestion, and enhancing safety, numerous theoretical models have been proposed to plan CAVmulti... Motivated by the promising benefits of connected and autonomous vehicles (CAVs) in improving fuelefficiency, mitigating congestion, and enhancing safety, numerous theoretical models have been proposed to plan CAVmultiple-step trajectories (time–specific speed/location trajectories) to accomplish various operations. However, limitedefforts have been made to develop proper trajectory control techniques to regulate vehicle movements to follow multiplesteptrajectories and test the performance of theoretical trajectory planning models with field experiments. Without aneffective control method, the benefits of theoretical models for CAV trajectory planning can be difficult to harvest. This studyproposes an online learning-based model predictive vehicle trajectory control structure to follow time–specific speed andlocation profiles. Unlike single-step controllers that are dominantly used in the literature, a multiple-step model predictivecontroller is adopted to control the vehicle’s longitudinal movements for higher accuracy. The model predictive controlleroutput (speed) cannot be interpreted by vehicles. A reinforcement learning agent is used to convert the speed value to thevehicle’s direct control variable (i.e., throttle/brake). The reinforcement learning agent captures real-time changes in theoperating environment. This is valuable in saving parameter calibration resources and improving trajectory control accuracy.A line tracking controller keeps vehicles on track. The proposed control structure is tested using reduced-scale robot cars.The adaptivity of the proposed control structure is demonstrated by changing the vehicle load. Then, experiments on twofundamental CAV platoon operations (i.e., platooning and split) show the effectiveness of the proposed trajectory controlstructure in regulating robot movements to follow time-specific reference trajectories. 展开更多
关键词 connected and autonomous vehicles(CAVs) reinforcement learning physical tests time-specific speed and location longitudinal and lateral control
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