ALBERT

Description: Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed a method to identify seed maize production fields as early as possible in the growing season using a time series of remote sensing images in the Liangzhou district of Gansu province, China. We collected Sentinel-2 and GaoFen-1 (GF-1) images captured from March to September. The feature space for classification consists of four original bands, namely red, green, blue, and near-infrared (nir), and eight vegetation indexes. We analyzed the timeliness of seed maize identification using Sentinel-2 time series of different time spans and identified the earliest time frame for reasonable classification accuracy. Then, the earliest time series that met the requirements of regulatory accuracy were compared and analyzed. Four machine/deep learning algorithms were tested, including K-nearest neighbor (KNN), support vector classification (SVC), random forest (RF), and long short-term memory (LSTM). The results showed that using Sentinel-2 images from March to June, the RF and LSTM algorithms achieve over 88% accuracy, with the LSTM performing the best (90%). In contrast, the accuracy of KNN and SVC was between 82% and 86%. At the end of June, seed maize mapping can be carried out in the experimental area, and the precision can meet the basic requirements of monitoring for the seed industry. The classification using GF-1 images were less accurate and reliable; the accuracy was 85% using images from March to June. To achieve near real-time identification of seed maize fields early in the growing season, we adopted an automated sample generation approach for the current season using only historical samples based on clustering analysis. The classification accuracy using new samples extracted from historical mapping reached 74% by the end of the season (September) and 63% by the end of July. This research provides important insights into the classification of crop fields cultivated with the same crop but different planting patterns using remote sensing images. The approach proposed by this study enables near-real time identification of seed maize production fields within the growing season, which could effectively support large-scale monitoring of the seed supply industry.

Description: Unmanned aerial vehicles (UAVs) received an unprecedented surge of people’s interest worldwide in recent years. This paper investigates the specific problem of cooperative mission planning for multiple UAVs on the battlefield from a hierarchical decision-making perspective. From the view of the actual mission planning issue, the two key problems to be solved in UAV collaborative mission planning are mission allocation and route planning. In this paper, both of these problems are taken into account via a hierarchical decision-making model. Firstly, we use a target clustering algorithm to divide the original targets into target subgroups, where each target subgroup contains multiple targets. Secondly, a fuzzy ant colony algorithm is used to calculate the global path between target subgroups for a single-target group. Thirdly, a fuzzy ant colony algorithm is also used to calculate the local path between multiple targets for a single-target subgroup. After three levels of decision-making, the complete path for multiple UAVs can be obtained. In order to improve the efficiency of a collaborative task between different types of UAVs, a cooperative communication strategy is developed, which can reduce the number of UAVs performing tasks. Finally, experimental results demonstrate the effectiveness of the proposed cooperative mission planning and cooperative communication strategy for multiple UAVs.

Description: This study deals with the CdS/CdTe solar cells under low illumination intensity, with cell #1 for the shunt resistance exceeding 100,000 Ω·cm2 and cell #2 for the shunt resistance above 1000 Ω·cm2. The diode parameter variations with the decline of the irradiance intensity are illustrated by dividing 0–100 mW/cm−2 into a number of small intensity ranges for J–V measurements and assuming the diode parameters to be constant within each range, the diode parameters of each range including the series resistance, the shunt resistance, the reverse saturation current density and the ideality factor are then extracted by employing an analytical approach. The mechanism of the cell performance deviations are also investigated by basic theories, reports and experiments. For cell #1 with higher Rsh corresponding to less traps, Rsh shows a upward tendency as the irradiance declines, n and J0 exhibit a rise with the irradiance and keep nearly unchanged at the low irradiance values mainly due to recombination and carrier contributions, Rs shows a slight increase when the irradiance intensity goes down because of the resistance of CdTe absorption layer. For cell #2 with lower Rsh corresponding to more traps, with the decrease of the illumination intensity, Rsh increases sharply only for captured carrier reduction, Rs goes steadily up similarly, n and J0 exhibit a decline with the irradiance due to recombination shift. It should be pointed out that Rs varies much smoother than the traditional approximation of a reciprocal of differential at short circuit, and the distribution of Rsh is diverse, and an average Rsh of for each intensity range can reflect the variation trend.

Description: The Longshoushan Metallogenic Belt (northwestern China) is known for its word-class Jinchuan Ni-Cu sulfide (Pt) deposit and is also an important uranium metallogenic belt. The Jiling uranium deposit in this belt is a typical Na-metasomatic uranium deposit, which rarely occurs in China. Mineralization in the Jiling uranium deposit is hosted in granitoids that have suffered a Na-metasomatic alteration. There are three kinds of uranium minerals, including uraninite, pitchblende, and coffinite in the Jiling uranium deposit. Pitchblende is the predominant uranium mineral. Integrating the mineralogy and geochemistry of uranium minerals, and in situ electron microprobe analyzer (EMPA) U-Th-Pb chemical dating, we aimed to unravel the age and nature of the mineralization, to decipher the characteristics of the hydrothermal alteration and the U mineralization process. Based on the microtextural features and compositional variations, primary uraninite was altered to uraninite A and B, and fresh pitchblende was altered to pitchblende A and B. The best-preserved uraninite crystals displayed a euhedral-shape with high Pb and low SiO2, CaO, FeO, and Al2O3 contents, and was interpreted as primary uraninite. The EMPA U-Th-Pb chemical ages revealed that uraninite may have formed at 435.9 ± 3.3 Ma. High ThO2 + ΣREE2O3 + Y2O3 contents illustrated that the best preserved uraninite crystallized at a high temperature. Altered pitchblende A showed a relatively brighter gray color in backscattered electron (BSE) images and with a lower SiO2 content than B. Three analysis spots of the fresh pitchblende showed low contents of ΣSiO2 + CaO, indicating no obvious alteration. EMPA U-Th-Pb chemical dating gave a mean chemical age of 361 Ma. The low Th + ΣREE2O3 contents indicated that this pitchblende formed at a relatively low temperature. According to the different characteristics of occurrence and chemical composition, the coffinite in the Jiling uranium deposit can be divided into coffinite A and B, respectively. The compositional variation of the fresh and altered uraninite and pitchblende indicated that both uraninite and pitchblende underwent at least two discrete hydrothermal fluid alterations. The U mineralization was divided into two stages; uraninite was formed at a high temperature and possibly from a magmatic-hydrothermal fluid during ore stage I. Then, pitchblende was formed at a low temperature, during ore stage II. According to the petrographic observations and their chemical compositions, coffinite A and B resulted from the alterations of uraninite and pitchblende, respectively.

Description: The Sports and Leisure Characteristic Town (SLCT) has become a reasonable strategy for enhancing the sustainability of new urbanization processes in rural China. Although the Chinese government has issued a series of policies to support the development of SLCTs, limited financial resources are a major obstacle. Thus, public-private partnerships (PPPs) have been increasingly encouraged for establishing SLCT projects. However, the factors that significantly influence private partners’ willingness to participate in SLCT PPP projects remain unclear. The authors of this paper conducted expert interviews and questionnaire surveys concerning the SLCT PPP projects that have been implemented in China, identifying 23 factors that can be grouped into five major components using principal component factor analysis. Multiple linear regression was then performed to evaluate the relationships between factors and private partners’ willingness to participate. The results indicated that the factors that critically influence private partners’ willingness to participate include stakeholder factors, the internal factors of private partners, external environmental risk factors, the supporting measure factors of SLCTs, and the locational factors of SLCTs. This work also offers suggestions for encouraging private partners to participate in SLCT PPP projects. This study can provide a theoretical basis and practical guidance for government and private partners in order to help them implement sustainable SLCT PPP projects.

Description: To improve the intelligence and accuracy of the Situation Assessment (SA) in complex scenes, this work develops an improved fuzzy deep neural network approach to the situation assessment for multiple Unmanned Aerial Vehicle(UAV)s. Firstly, this work normalizes the scene data based on time series and use the normalized data as the input for an improved fuzzy deep neural network. Secondly, adaptive momentum and Elastic SGD (Elastic Stochastic Gradient Descent) are introduced into the training process of the neural network, to improve the learning performance. Lastly, in the real-time situation assessment task for multiple UAVs, conventional methods often bring inaccurate results for the situation assessment because these methods don’t consider the fuzziness of task situations. This work uses an improved fuzzy deep neural network to calculate the results of situation assessment and normalizes these results. Then, the degree of trust of the current result, relative to each situation label, is calculated with the normalized results using fuzzy logic. Simulation results show that the proposed method outperforms competitors.

Description: In this work, a bidirectional grating coupler for perfectly vertical coupling is proposed. The coupling efficiency is enhanced using a silicon nitride (Si3N4) layer above a uniform grating. In the presence of Si3N4 layer, the back-reflected optical power into the fiber is diminished and coupling into the waveguide is increased. Genetic algorithm (GA) is used to optimize the grating and Si3N4 layer simultaneously. The optimal design obtained from GA shows that the average in-plane coupling efficiency is enhanced from about 57.5% (−2.5 dB) to 68.5% (−1.65 dB), meanwhile the average back-reflection in the C band is reduced from 17.6% (−7.5 dB) to 7.4% (−11.3 dB). With the help of a backside metal mirror, the average coupling efficiency and peak coupling efficiency are further increased to 87% (−0.6 dB) and 89.4% (−0.49 dB). The minimum feature size of the designed device is 266 nm, which makes our design easy to fabricate through 193 nm deep-UV lithography and lowers the fabrication cost. In addition, the coupler proposed here shows a wide-band character with a 1-dB bandwidth of 64 nm and 3-dB bandwidth of 96 nm. Such a grating coupler design can provide an efficient and cost-effective solution for vertical fiber-to-chip optical coupling of a Wavelength Division Multiplexing (WDM) application.

Description: Fe-Cr-Co alloys precipitate nanosized α1 particles through spinodal decomposition, and their magnetic performance is susceptible to influence by the shape and arrangement of α1 particles. We studied spinodal decomposition during the heat treatment of Fe-Cr-Co alloys by both experimental and numerical simulation. Fe-Cr-Co alloys were fabricated first by directional solidification, followed by thermomagnetic treatment in a high magnetic field (HMF) and step aging. The experimental results show a spinodally decomposed structure consisting of nanosized α1 particles. The applied HMF caused the α1 phase to change into a rod-like shape. Moreover, a feather-like structure was observed near the grain boundary (GB), with slim α1 rods regularly arranged along the direction perpendicular to the GB. With the shape change and alignment of the α1 phase in the HMF, Fe-Cr-Co alloys show magnetic coercivity that is superior to those of samples without an HMF. To reveal the influence of HMF on phase transformations and the effect of GB, we conducted phase-field simulations of spinodal decomposition in the Fe-Cr-Co alloy. A migrating GB contributes to the elongation and arrangement of the α1 phase in the regions where the GB has passed. Thus, the α1 phase is arranged as parallel rods that are perpendicular to the GB. This GB effect consists of the effect of enhanced atomic mobility and the elastic energy. The α1 rods are elongated along the direction of HMF. The simulation results indicate that the feather-like structure is caused by a combined effect of both the GB and HMF. It is shown that the model generates microstructures which are qualitatively similar to those observed experimentally.