An Algorithm of Reinforcement Learning for Maneuvering Parameter Self-Tuning Applying in Satellite Cluster

Satellite cluster is a type of artificial cluster, which is attracting wide attention at present. Although the traditional empirical parameter method (TEPM) has the potential to deal with the mission of satellite flocking, it is difficult to select the proper parameters. In order to improve the flight effect in the problem of satellite cluster, as well as to make the selection of flight parameters more reasonable, the traditional sensing zones are improved. A 3σ position error ellipsoid and an induction ellipsoid are applied for substituting the traditional repulsing zone and attracting zone, respectively. Besides, we propose an algorithm of reinforcement learning for parameter self-tuning (RLPST), which is based on the actor-critic framework, to automatically learn the suitable flight parameters. To obtain the parameters in the repulsing zone, orientating zone, and attracting zone of each member in the cluster, a three-channel learning framework is designed. The learning process makes the framework finally find the suitable parameters. Numerical experimental results have shown the superiorities compared to the traditional method, which include trajectory deviation and sensing rate or terminal matching rate, as well as the improvement of the flight paths under the learning framework

Genetic diversity analysis and variety identification using SSR and SNP markers in melon

Abstract Melon is an important horticultural crop with a pleasant aromatic flavor and abundance of health-promoting substances. Numerous melon varieties have been cultivated worldwide in recent years, but the high number of varieties and the high similarity between them poses a major challenge for variety evaluation, discrimination, as well as innovation in breeding. Recently, simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs), two robust molecular markers, have been utilized as a rapid and reliable method for variety identification. To elucidate the genetic structure and diversity of melon varieties, we screened out 136 perfect SSRs and 164 perfect SNPs from the resequencing data of 149 accessions, including the most representative lines worldwide. This study established the DNA fingerprint of 259 widely-cultivated melon varieties in China using Target-seq technology. All melon varieties were classified into five subgruops, including ssp. agrestis, ssp. melo, muskmelon and two subgroups of foreign individuals. Compared with ssp. melo, the ssp. agrestis varieties might be exposed to a high risk of genetic erosion due to their extremely narrow genetic background. Increasing the gene exchange between ssp. melo and ssp. agrestis is therefore necessary in the breeding procedure. In addition, analysis of the DNA fingerprints of the 259 melon varieties showed a good linear correlation (R2 = 0.9722) between the SSR genotyping and SNP genotyping methods in variety identification. The pedigree analysis based on the DNA fingerprint of ‘Jingyu’ and ‘Jingmi’ series melon varieties was consistent with their breeding history. Based on the SNP index analysis, ssp. agrestis had low gene exchange with ssp. melo in chromosome 4, 7, 10, 11and 12, two specific SNP loci were verified to distinguish ssp. agrestis and ssp. melon varieties. Finally, 23 SSRs and 40 SNPs were selected as the core sets of markers for application in variety identification, which could be efficiently applied to variety authentication, variety monitoring, as well as the protection of intellectual property rights in melon

Comparison of DUS testing and SNP fingerprinting for variety identification in cucumber

Variety identification plays an important role in protecting the intellectual property of varieties, ensuring seed quality, and encouraging breeding innovation. Currently, morphological evaluation in the field, such as distinctness, uniformity, and stability (DUS) testing, and DNA fingerprinting in the laboratory using molecular markers are two dominant methods used for variety identification. Few studies have compared the results of these approaches, and the relationship between the two methods is obscure. In this study, 134 dominant cucumber varieties were evaluated using 50 DUS testing traits and genotyped by 40 single nucleotide polymorphisms (SNPs). The 40 SNPs were developed in our previous study and are well suited for variety identification. In the DUS testing, significant positive or negative correlations among 50 DUS traits were observed, and 20 core traits, including 15 fruit traits, were further selected to increase field inspection efficiency. This suggested that fruit shape plays an important role in variety identification. The ratio of fruit length/diameter was the most important trait, explaining 9.2% of the phenotypic variation. In the DNA fingerprinting test, the 40 SNPs were highly polymorphic and could distinguish all of the 134 cucumber varieties, and 14 core SNPs were selected to improve the identification rate. Interestingly, the population structure analysis of 134 cucumber varieties by phenotypic data in the DUS test was in accordance with the genotypic data from the DNA fingerprinting, indicating that all varieties could be divided into the same four subgroups: European type, North China type, South China type, and hybrids of the North China and South China types. Moreover, linear correlativity of distinguishment for each pair of varieties was observed between the DUS test and the DNA fingerprinting. These results indicated that these two methods have good application in future research, especially for the scaled-up analysis of hundreds of varieties