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The network real-time kinematic (RTK) technique uses continuously operating reference stations (CORS) within a geographic area to model the distance dependent errors, allowing users in the area to solve ambiguities. A key step in network RTK is to fix ambiguities between multiple reference stations. When a new satellite rises or when maintenance happens, many unknown parameters are involved in the mathematical model, and traditional methods take some time to estimate the integer ambiguities reliably. The purpose of this study is the single-epoch ambiguity resolution on small-scale CORS network with inter-station distance of around 50 km. A new differencing scheme is developed to explore the full potential of multi-frequency Global Navigation Satellite System (GNSS). In this scheme, a differencing operation is formed between satellites with the closest mapping functions. With the new differencing scheme, tropospheric error can be mostly neglected after the correction, as well as the double-differencing operation. Numerical tests based on two baselines of 49 km and 35 km show that the success rate of ambiguity resolution can reach more than 90%. The single-epoch ambiguity resolution for reference stations brings many benefits to the network RTK service, for example, the instantaneous recovery after maintenance or when a new satellite rises.

Abstract Based on the large volume Gaia Early Data Release 3 and LAMOST Data Release 5 data, we estimate the bias-corrected binary fractions of the field late G and early K dwarfs. A stellar locus outlier method is used in this work, which works well for binaries of various periods and inclination angles with single-epoch data. With a well-selected, distance-limited sample of about 90,000 GK dwarfs covering wide stellar chemical abundances, it enables us to explore the binary fraction variations with different stellar populations. The average binary fraction is 0.42 ± 0.01 for the whole sample. Thin-disk stars are found to have a binary fraction of 0.39 ± 0.02, thick-disk stars have a higher one of 0.49 ± 0.02, while inner halo stars possibly have the highest binary fraction. For both the thin- and thick-disk stars, the binary fractions decrease toward higher [Fe/H], [α/H], and [M/H] abundances. However, the suppressing impacts of [Fe/H], [α/H], and [M/H] are more significant for the thin-disk stars than those for the thick-disk stars. For a given [Fe/H], a positive correlation between [α/Fe] and the binary fraction is found for the thin-disk stars. However, this tendency disappears for the thick-disk stars. We suspect that it is likely related to the different formation histories of the thin and thick disks. Our results provide new clues for theoretical works on binary formation.

Abstract LAMOST Data Release 5, covering ∼17 000 deg2 from –10° to 80° in declination, contains 9 million co-added low-resolution spectra of celestial objects, each spectrum combined from repeat exposure of two to tens of times during Oct 2011 to Jun 2017. In this paper, we present the spectra of individual exposures for all the objects in LAMOST Data Release 5. For each spectrum, the equivalent width of 60 lines from 11 different elements are calculated with a new method combining the actual line core and fitted line wings. For stars earlier than F type, the Balmer lines are fitted with both emission and absorption profiles once two components are detected. Radial velocity of each individual exposure is measured by minimizing χ 2 between the spectrum and its best template. The database for equivalent widths of spectral lines and radial velocities of individual spectra are available online. Radial velocity uncertainties with different stellar type and signal-to-noise ratio are quantified by comparing different exposure of the same objects. We notice that the radial velocity uncertainty depends on the time lag between observations. For stars observed in the same day and with signal-to-noise ratio higher than 20, the radial velocity uncertainty is below 5km s−1, and increases to 10 km s−1 for stars observed in different nights.

Precise point positioning (PPP) with ambiguity resolution (AR) can improve positioning accuracy and reliability. The narrow-lane (NL) AR solution can reach centimeter-level accuracy but there is a certain initialization time. In contrast, extra-wide-lane (EWL) or wide-lane (WL) ambiguity can be fixed instantaneously. However, due to the limited correction accuracy of the empirical atmospheric model, the positioning accuracy is only a few decimeters. In order to further improve the real-time performance of PPP while ensuring accuracy, we developed a multi-system multi-frequency uncombined PPP single-epoch EWL/WL/NL AR method with regional atmosphere modelling. In the proposed method, the precise atmosphere, including zenith wet-troposphere delay (ZWD) and the slant ionosphere, is extracted through multi-frequency stepwise AR, which then is both interpolated and broadcast to users. By adding regional atmosphere constraints, users can achieve single-epoch PPP AR with centimeter-level accuracy. To verify the algorithm, four sets of reference networks with different inter-station distances are used for experiments. With atmosphere constraints, the accuracy of the single-epoch WL solution can be improved from the decimeter level to a few centimeters, with an improvement of more than 90%, and the epoch fix rate can also be improved to varying degrees, especially for the dual-frequency case. Due to the enlarged noise of the EWL combination, its accuracy is at the decimeter level, while the accuracy of the WL/NL solution can reach several centimeters. However, reliable NL ambiguity-fixing tightly relies on atmosphere constraints with sufficiently high accuracy. When the modelling of the atmosphere correction is not accurate enough, the NL AR performance is degraded, although this situation can be improved to a certain extent through the multi-GNSS combination. In contrast, in this case, the WL ambiguity can be successfully fixed and can support the precise positioning with an accuracy of several centimeters.

GNSS-only attitude determination is difficult to perform well in poor-satellite-tracking environments such as urban areas with high and dense buildings or trees. In addition, it is harder to resolve integer ambiguity in the case of single-frequency single-epoch process mode. In this contribution, a low-cost MEMS gyroscope is integrated with multi-antenna GNSS to improve the performance of the attitude determination. A new tightly coupled (TC) model is proposed, which uses a single filter to achieve the optimal estimation of attitude drift, gyro biases and ambiguities. In addition, a MEMS-Attitude-aided Quality-Control method (MAQC) for GNSS observations is designed to eliminate both the carrier multipath errors and half-cycle slips disturbing ambiguity resolution. Vehicle experiments show that in GNSS-friendly scenarios, the Ambiguity Resolution (AR) success rate of the proposed model with MAQC can reach 100%, and the accuracy of attitudes are (0.12, 0.2, 0.2) degrees for heading, pitch and roll angles, respectively. Even in harsh scenarios, the AR success rate increases from about 67% for the GNSS only case to above 90% after coupling GNSS tightly with MEMS, and it is further improved to about 98% with MAQC. Meanwhile, the accuracy and continuity of attitude determination are effectively guaranteed.

In order to learn the mappings from words to referents, children must integrate co-occurrence information across individually ambiguous pairs of scenes and utterances, a challenge known as cross-situational word learning. In machine learning, recent multimodal neural networks have been shown to learn meaningful visual-linguistic mappings from cross-situational data, as needed to solve problems such as image captioning and visual question answering. These networks are potentially appealing as cognitive models because they can learn from raw visual and linguistic stimuli, something previous cognitive models have not addressed. In this paper, we examine whether recent machine learning approaches can help explain various behavioral phenomena from the psychological literature on cross-situational word learning. We consider two variants of a multimodal neural network architecture, and look at seven different phenomena associated with cross-situational word learning, and word learning more generally. Our results show that these networks can learn word-referent mappings from a single epoch of training, matching the amount of training found in cross-situational word learning experiments. Additionally, these networks capture some, but not all of the phenomena we studied, with all of the failures related to reasoning via mutual exclusivity. These results provide insight into the kinds of phenomena that arise naturally from relatively generic neural network learning algorithms, and which word learning phenomena require additional inductive biases.

Abstract. Container crane inspection is a very important task to maintain their uninterrupted operation. Nevertheless, this is a costly and time-consuming activity if performed manually. Recently, image-based detection of surface damages or changes using drones has gained increasing interest in industry; especially when objects of interest have a complex structure like container cranes. One main aim of this paper is a single-epoch image analysis which will also serve later for multi-epoch processing. It provides reliable information about current defects that may lead to big damages if not inspected by experts. Naïve Bayes classifier is employed to classify the images in different classes of which critical defects and especially rust is important. The preliminary results show that the precision on the target class reached about 99%. However, 87% percent recall in this class is not enough and it should be improved for this application.Having a large dataset requires an efficient data management system to provide users and decision makers with the information needed. In addition, in order to foster full automation, the aforementioned image analysis component should have a direct connection to the database and thus is able to query image and semantic information. We therefore introduce the second aim of our research, that is a concept for database design. Here, not only the raw data and the final results are integrated but also the intermediate results. At the same time, the database concept is connected to an integrated client interface that allows retrieving data of interest in a virtual globe.

As the signals of Galileo and the global BDS-3 navigation satellite system have been accessible, positioning users can use quad-frequency even five-frequency signals nowadays. With multifrequency signals, one can form some useful combinations to improve the positioning performance, e.g., the widely used extra-wide-lane (EWL)/wide-lane (WL) in triple-frequency cases. For quad-frequency or five-frequency cases, better positioning performance can be expected since additional frequencies are introduced. In this study, we systematically analyse the benefits of Galileo and BDS-3 quad-frequency signals on long-baseline instantaneous positioning. First, the theoretical analysis of EWL/WL ambiguity resolution (AR) and satellite-station range estimation with a single-satellite geometry-free and ionosphere-free model is studied, along with the comparison with triple-frequency cases. Second, using the quad-frequency advantages, an instantaneous decimeter-level positioning model is proposed, where the geometry-free model is adopted for the first two EWL AR and the geometry-based model is adopted for the third WL AR. In the end, the AR and positioning performance are evaluated using real long-baseline date containing Galileo and BDS-3 quad-frequency observations. The results indicate that, with quad-frequency observations, both Galileo and BDS-3 EWL/WL ambiguities can be fixed reliably with a single epoch. Contributed by the resolved EWL/WL ambiguities, instantaneous decimeter-level positioning can be obtained, with the accuracies of 0.116 m/0.126 m/0.351 m in north, east, and up directions, respectively.