The region coverage control problem of multiple stratospheric airships system is firstly addressed in this paper.Towards it,we propose a two-layer control framework with the artificial potential field(APF)-based regio...The region coverage control problem of multiple stratospheric airships system is firstly addressed in this paper.Towards it,we propose a two-layer control framework with the artificial potential field(APF)-based region coverage control law and the adaptive tracking control law.The APF-based region coverage control law ensures the coverage task is achieved until every single stratospheric airship ends up performing station keeping where near the respective global minimum point,in which an innovative solution to the local minimum problem is put forward.The adaptive tracking control law is designed to realize motion control using tracking the desired velocity and angular velocity given by coverage control law,with the consideration of several practical control problems as unknown individual differences and external disturbances.To save resources,the combined self-/event-triggered mechanism designed therein significantly reduces the times of state information transmission and control law calculation.The effectiveness of the proposed control framework is verified through simulations.展开更多
The work described in this paper is a study of the estimation of copper, silver and gold coverages on the iridium field emitter tip surface. The study has been carried out by using a simple field emission microscope d...The work described in this paper is a study of the estimation of copper, silver and gold coverages on the iridium field emitter tip surface. The study has been carried out by using a simple field emission microscope designed especially for the purpose of the adsorbate coverage calibration. It was equipped with an iridium field emitter tip. On one side of the microscope was the vapor source 12.5 cm from the tip, and on the other side 16.2 cm from the source was a quartz crystal oscillator. The crystal leads were spot welded to a two-pin tungsten-glass press-seal. In front of the crystal, a nickel shield was mounted in which there was a circular hole of an area of 0.0804 cm2, slightly smaller than the surface of the crystal, to prevent shorting of the conducting ends of the crystal which would be brought about by the condensed metal. The sensing crystal inside the microscope was driven by a small circuit placed just outside the microscope. The driving circuit was in turn connected to another circuit which comprised a frequency comparator unit which could read the frequency of the quartz crystal oscillator before and after the deposition of the adsorbate and gave a direct digital reading of ?(f is the resonance frequency of the crystal before the deposition of the adsorbate and Δf is the difference in the frequency of the oscillator after and before the deposition of the adsorbate on the crystal). The mass added to either side of the crystal alters its resonant frequency. The frequency shift obtained for a certain thickness of the deposited film depends on the density of the deposited film [1] [2].展开更多
Energy efficiency and sensing coverage are essential metrics for enhancing the lifetime and the utilization of wireless sensor networks. Many protocols have been developed to address these issues, among which, cluster...Energy efficiency and sensing coverage are essential metrics for enhancing the lifetime and the utilization of wireless sensor networks. Many protocols have been developed to address these issues, among which, clustering is considered a key technique in minimizing the consumed energy. However, few clustering protocols address the sensing coverage metric. This paper proposes a general framework that addresses both metrics for clustering algorithms in wireless sensor networks. The proposed framework is based on applying the principles of Virtual Field Force on each cluster within the network in order to move the sensor nodes towards proper locations that maximize the sensing coverage and minimize the transmitted energy. Two types of virtual forces are used: an attractive force that moves the nodes towards the cluster head in order to reduce the energy used for communication and a repulsive force that moves the overlapping nodes away from each other such that their sensing coverage is maximized. The performance of the proposed mechanism was evaluated by applying it to the well-known LEACH clustering algorithm. The simulation results demonstrate that the proposed mechanism improves the performance of the LEACH protocol considerably in terms of the achieved sensing coverage, and the network lifetime.展开更多
Crop coverage(CC)is an important parameter to represent crop growth characteristics,and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions.In this study,a no...Crop coverage(CC)is an important parameter to represent crop growth characteristics,and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions.In this study,a novel CNN-LSTM model that combined the advantages of convolutional neural network(CNN)in feature extraction and long short-term memory(LSTM)in time series processing was proposed for multi-day ahead forecasting of maize CC.Considering the influence of climate change on maize growth,five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model.The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM.The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R2 at all forecasting horizons.Subsequently,the performance of CNN-LSTM under univariate(historical maize CC)and multivariate(historical maize CC+microclimatic factors)input was compared,and the results indicated that additional microclimatic factors were effective in improving the forecasting performance.Furthermore,the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed,and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage.Therefore,CNN-LSTM can be considered a useful tool to forecast maize CC.展开更多
基金supported by the Postdoctoral Science Foundation of China(Grant No.2020TQ0028)the National Natural Science Foundation of China(No.62173016)Beijing Natural Science Foundation,PRChina(No.4202038)。
文摘The region coverage control problem of multiple stratospheric airships system is firstly addressed in this paper.Towards it,we propose a two-layer control framework with the artificial potential field(APF)-based region coverage control law and the adaptive tracking control law.The APF-based region coverage control law ensures the coverage task is achieved until every single stratospheric airship ends up performing station keeping where near the respective global minimum point,in which an innovative solution to the local minimum problem is put forward.The adaptive tracking control law is designed to realize motion control using tracking the desired velocity and angular velocity given by coverage control law,with the consideration of several practical control problems as unknown individual differences and external disturbances.To save resources,the combined self-/event-triggered mechanism designed therein significantly reduces the times of state information transmission and control law calculation.The effectiveness of the proposed control framework is verified through simulations.
文摘The work described in this paper is a study of the estimation of copper, silver and gold coverages on the iridium field emitter tip surface. The study has been carried out by using a simple field emission microscope designed especially for the purpose of the adsorbate coverage calibration. It was equipped with an iridium field emitter tip. On one side of the microscope was the vapor source 12.5 cm from the tip, and on the other side 16.2 cm from the source was a quartz crystal oscillator. The crystal leads were spot welded to a two-pin tungsten-glass press-seal. In front of the crystal, a nickel shield was mounted in which there was a circular hole of an area of 0.0804 cm2, slightly smaller than the surface of the crystal, to prevent shorting of the conducting ends of the crystal which would be brought about by the condensed metal. The sensing crystal inside the microscope was driven by a small circuit placed just outside the microscope. The driving circuit was in turn connected to another circuit which comprised a frequency comparator unit which could read the frequency of the quartz crystal oscillator before and after the deposition of the adsorbate and gave a direct digital reading of ?(f is the resonance frequency of the crystal before the deposition of the adsorbate and Δf is the difference in the frequency of the oscillator after and before the deposition of the adsorbate on the crystal). The mass added to either side of the crystal alters its resonant frequency. The frequency shift obtained for a certain thickness of the deposited film depends on the density of the deposited film [1] [2].
文摘Energy efficiency and sensing coverage are essential metrics for enhancing the lifetime and the utilization of wireless sensor networks. Many protocols have been developed to address these issues, among which, clustering is considered a key technique in minimizing the consumed energy. However, few clustering protocols address the sensing coverage metric. This paper proposes a general framework that addresses both metrics for clustering algorithms in wireless sensor networks. The proposed framework is based on applying the principles of Virtual Field Force on each cluster within the network in order to move the sensor nodes towards proper locations that maximize the sensing coverage and minimize the transmitted energy. Two types of virtual forces are used: an attractive force that moves the nodes towards the cluster head in order to reduce the energy used for communication and a repulsive force that moves the overlapping nodes away from each other such that their sensing coverage is maximized. The performance of the proposed mechanism was evaluated by applying it to the well-known LEACH clustering algorithm. The simulation results demonstrate that the proposed mechanism improves the performance of the LEACH protocol considerably in terms of the achieved sensing coverage, and the network lifetime.
基金financially supported by the National Natural Science Foundation of China(Grant No.61772240No.51961125102)the 111 Project(B12018).
文摘Crop coverage(CC)is an important parameter to represent crop growth characteristics,and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions.In this study,a novel CNN-LSTM model that combined the advantages of convolutional neural network(CNN)in feature extraction and long short-term memory(LSTM)in time series processing was proposed for multi-day ahead forecasting of maize CC.Considering the influence of climate change on maize growth,five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model.The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM.The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R2 at all forecasting horizons.Subsequently,the performance of CNN-LSTM under univariate(historical maize CC)and multivariate(historical maize CC+microclimatic factors)input was compared,and the results indicated that additional microclimatic factors were effective in improving the forecasting performance.Furthermore,the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed,and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage.Therefore,CNN-LSTM can be considered a useful tool to forecast maize CC.
