ALBERT

Description: Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.

Description: The receiver is a signal receiving device in a radio telescope system. As an important parameter to characterize the receiver performance, noise temperature is very practical to calibrate accurately. The traditional receiver noise temperature calibration method is the cold and ambient load method. Through the establishment of K-band ambient receiver, and its amplitude calibration test platform of the cold and ambient load method, chopper wheel method, and ambient and hot load method, comparison and analysis of the above three methods were carried out. The test and calculation results show that the test accuracy of the cold and ambient load method is about 1.3%, that of the chopper wheel method (nonlow elevation) is about 3%, and that of the ambient and hot load method is about 9%. The test accuracy of the ambient and hot load method is slightly lower than that of the above two methods. The analysis is mainly due to the uncertainty of the hot load temperature and the small temperature difference between the two loads, which leads to the deterioration of the overall accuracy. But the advantage is that the method can perform real-time calibration in the process of observation, and it is easier to implement than the traditional cold and ambient load method. The results of noise temperature measurement are compared with those of theoretical calculation, the error is basically within 10%, and it can satisfy the demand of the noise temperature test. In the future, we expect that on the basis of increasing the hot load temperature, further experiments were carried out on the thermostatic treatment of hot load and the accuracy of temperature acquisition, and finally we hope that this method can better meet the testing requirements of receiver noise temperature and radio source amplitude calibration.

Description: Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development of multisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.

Description: Designing routing protocols in Low power and Lossy Networks (LLNs) imposes great challenges. In emergency scenarios, the large and rapid data traffic caused by emergencies will lead to network congestion and bring about significant packet loss and delay. Routing protocol for LLNs (RPL) is the IETF standard for IPv6 routing in LLNs. The basic version of RPL uses Expected Transmission Count (ETX) as the default routing metric; it cannot solve the problem of sudden large data traffic. In this paper, we propose a congestion avoidance multipath routing protocol which uses composite routing metrics based on RPL, named CA-RPL. A routing metric for RPL that minimized the average delay towards the DAG root is proposed, and the weight of each path is computed by four metrics. The mechanism is explained and its performance is evaluated through simulation experiments based on Contiki. Simulation results show that the proposed CA-RPL reduces the average time delay by about 30% compared to original RPL when the interpacket interval is short and has almost 20% reduction in packet loss ratio. The CA-RPL can effectively alleviate the network congestion in the network with poor link quality and large data traffic and significantly improve the performance of LLNs.

Description: For railway companies, the benefits from revenue management activities, like inventory control, dynamic pricing, and so forth, rely heavily on the accuracy of the short-term forecasting of the passenger flow. In this paper, based on the analysis of the relevance between final booking amounts and shapes of the booking curves, a novel short-term forecasting approach, which employs a specifically designed clustering algorithm and the data of both historical booking records and the bookings on hand, is proposed. The empirical study with real data sets from Chinese railway shows that the proposed approach outperforms the advanced pickup model (one of the most popular models in practice) during the early and middle stages of booking horizon when bookings are not concentrated in the final days before departure.

Description: We develop a reliability model for systems with s-dependent degradation processes using copulas. The proposed model accommodates assumptions of s-dependence among degradation processes and allows for different marginal distributions. This flexibility makes the model more attractive compared with the multivariate distribution model, which lay on the limitation of the homogeneous marginal distribution and can only describe linear correlation. Marginal degradation process is modeled by the inverse Gaussian (IG) process with time scale transformation. Furthermore, we incorporate random drift to account for the possible heterogeneity in population. This paper also develops the statistical inference method using EM algorithm with two-stage procedure. The comparison results of the reliability estimation under both s-dependent and s-independent assumptions are illustrated in the illustrative example to demonstrate the applicability of the proposed method.

Description: This paper is devoted to introduce a novel method of the operational matrix of integration for Legendre wavelets in order to predict the thermal behavior of stratospheric balloons on float at high altitude in the stratosphere. Radiative and convective heat transfer models are also developed to calculate absorption and emission heat of the balloon film and lifting gas within the balloon. Thermal equilibrium equations (TEE) for the balloon system at daytime and nighttime are shown to predict the thermal behavior of stratospheric balloons. The properties of Legendre wavelets are used to reduce the TEE to a nonlinear system of algebraic equations which is solved by using a suitable numerical method. The approximations of the thermal behavior of the balloon film and lifting gas within the balloon are derived. The diurnal variations of the film and lifting gas temperature at float conditions are investigated, and the efficiency of the proposed method is also confirmed.

Description: Groundwater over-pumping in estuary cities leads to a series of groundwater environmental problems that seriously restricts economic development. On the basis of field investigation and long-term monitoring data analysis, a three-dimensional numerical model was built in the estuary of the Daqing River in Liaodong Bay, China. The Quaternary overburden can be generalized into five layers according to particle composition and parameters in the vertical direction. There are many scattered irrigation wells pumping in the second layer, and three water source areas mainly pumping groundwater in the fourth layer. Long-term over-pumping in multi-layered aquifers causes onshore layered seawater intrusion. The laws of layered intrusion under the layered pumping were calculated and analyzed with SEAWAT-2000, and the sensitivity was analyzed with the Sobol method. Results showed that the intrusion area had an obvious layered law. Layered pumping directly affected the layered intrusion area, as different permeability, tide and barrage further affected it. The prediction study showed that the cone of depression recovered after the pumping-limit of water source areas, and the intrusion area started to retreat in the fourth layer. At that time, the pumping quantity of irrigation wells became the main reason for the increase of the intrusion area. If the water source areas are used to bear part of the irrigation demand, so as to reduce the pressure of pumping in the second layer, the overall intrusion area can be reduced by about 0.23 km2 under the same pumping quantity.

Description: Tidal flats, which are non-vegetated land–sea transition areas, have an important ecological function in the global ecosystem. However, they have been shrinking in recent years due to natural and anthropogenic activities. Although many studies focus on tidal flats in the Yangtze River estuary (YRE) in China, how reclamation and plant invasion affect the expansion and erosion of tidal flats are still unclear. In this study, we analyzed all of the available Landsat TM/ETM+ /OLI imagery from the period 1996 to 2018 using the Google Earth Engine (GEE) cloud computing platform to obtain annual maps of coastal tidal flats of YRE at 30 m spatial resolution. We chose three sample tidal flats where severe Spartina alterniflora (S. alterniflora) invasion, reclamation, and control areas existed to explore the joint impact of plant invasion and reclamation on tidal flats. We also point out the main driving factor of tidal flat expansion of each island in YRE by multiple linear regression. Our results suggest that the tidal flats of YRE had obvious expansion from 1996 to 2018, and the speed of expansion is getting slower because of the decreasing deposits in the Yangtze River. Invasive S. alterniflora is effective at promoting silting, and tidal flats with S. alterniflora invasion expanded 2.54 times faster than the control group. Chongming and Hengsha Islands were mainly affected by sediment concentration, while Changxing and Jiuduansha Islands were affected by reclamation and S. alterniflora invasion, respectively. The results could be used to support coastal zone management and biodiversity conservation of the YRE.

Description: It is challenging to avoid obstacles safely and efficiently for multiple robots of different shapes in distributed and communication-free scenarios, where robots do not communicate with each other and only sense other robots’ positions and obstacles around them. Most existing multi-robot collision avoidance systems either require communication between robots or require expensive movement data of other robots, like velocities, accelerations and paths. In this paper, we propose a map-based deep reinforcement learning approach for multi-robot collision avoidance in a distributed and communication-free environment. We use the egocentric local grid map of a robot to represent the environmental information around it including its shape and observable appearances of other robots and obstacles, which can be easily generated by using multiple sensors or sensor fusion. Then we apply the distributed proximal policy optimization (DPPO) algorithm to train a convolutional neural network that directly maps three frames of egocentric local grid maps and the robot’s relative local goal positions into low-level robot control commands. Compared to other methods, the map-based approach is more robust to noisy sensor data, does not require robots’ movement data and considers sizes and shapes of related robots, which make it to be more efficient and easier to be deployed to real robots. We first train the neural network in a specified simulator of multiple mobile robots using DPPO, where a multi-stage curriculum learning strategy for multiple scenarios is used to improve the performance. Then we deploy the trained model to real robots to perform collision avoidance in their navigation without tedious parameter tuning. We evaluate the approach with multiple scenarios both in the simulator and on four differential-drive mobile robots in the real world. Both qualitative and quantitative experiments show that our approach is efficient and outperforms existing DRL-based approaches in many indicators. We also conduct ablation studies showing the positive effects of using egocentric grid maps and multi-stage curriculum learning.