A Decentralized Processing Schema for Efficient and Robust Real-time Multi-GNSS Satellite Clock Estimation

Real-time multi-GNSS precise point positioning (PPP) requires the support of high-rate satellite clock corrections. Due to the large number of ambiguity parameters, it is difficult to update clocks at high frequency in real-time for a large reference network. With the increasing number of satellites of multi-GNSS constellations and the number of stations, real-time high-rate clock estimation becomes a big challenge. In this contribution, we propose a decentralized clock estimation (DECE) strategy, in which both undifferenced (UD) and epoch-differenced (ED) mode are implemented but run separately in different computers, and their output clocks are combined in another process to generate a unique product. While redundant UD and/or ED processing lines can be run in offsite computers to improve the robustness, processing lines for different networks can also be included to improve the clock quality. The new strategy is realized based on the Position and Navigation Data Analyst (PANDA) software package and is experimentally validated with about 110 real-time stations for clock estimation by comparison of the estimated clocks and the PPP performance applying estimated clocks. The results of the real-time PPP experiment using 12 global stations show that with the greatly improved computational efficiency, 3.14 cm in horizontal and 5.51 cm in vertical can be achieved using the estimated DECE clock

Automatic Image Annotation Based on Particle Swarm Optimization and Support Vector Clustering

With the progress of network technology, there are more and more digital images of the internet. But most images are not semantically marked, which makes it difficult to retrieve and use. In this paper, a new algorithm is proposed to automatically annotate images based on particle swarm optimization (PSO) and support vector clustering (SVC). The algorithm includes two stages: firstly, PSO algorithm is used to optimize SVC; secondly, the trained SVC algorithm is used to annotate the image automatically. In the experiment, three datasets are used to evaluate the algorithm, and the results show the effectiveness of the algorithm

The Allocation of Prizes in Crowdsourcing Contests

A unique characteristic of crowdsourcing contest is the coexistence of multiple contests and each individual contestant strategically chooses the contest that maximizes his/her expected gain. The competition between contests for contestants significantly changes the optimal allocation of prizes for contest organizers. We show that the contestants with higher ability prefer to single-prize contests while those with lower ability prefer to multiple-prize contests, which makes single-prize contest is no longer the optimal choice for organizers as it was in the context of a single contest. We demonstrate that the organizers may allocate multiple prizes whether they intent to maximize total efforts or highest efforts, and presents the condition under which the multiple-prize approach will be optimal

Research on the Operation of Changzhou Distribution Center of Huitong Express in the Peak Period of Online Shopping

With the rapid development of network technology and the improvement of payment tools, the advantages of online shopping have become increasingly prominent, and various online shopping festivals have rapidly emerged. During the peak period of online shopping and the peak season of express delivery business, the market needs don’t often be met, and problems such as warehouse explosions, express delivery delays, and false receipts often occur. As an important transfer link of express logistics, Distribution Centers will affect the entire express logistics process once operational problems occur. This topic takes Huitong Express Changzhou Distribution Center as an example. By analyzing the current operating conditions of various departments of Huitong Express Changzhou Distribution Center and the problems that arise during the peak period of online shopping (taking Double 11 as an example), we will further explore more efficient to achieve the maximum possible profit by reducing costs in order to achieve the maximum possible profit

Borrower’s Self-Disclosure of Social Media Information in P2P Lending

In peer-to-peer (P2P) lending, soft information, such as borrowers’ facial features, textual descriptions of loan applications and so on, are regarded as potential signals to screen borrowers. In this study, we examine the signaling effect of a new category of soft information- social media information. Leveraging a unique dataset that combines loan data from a large P2P lending company with social media presence data from a popular social media site, and two natural experiments, we find two forms of social media information that act as signals of borrowers’ creditworthiness. First, borrowers’ choice to self-disclose their social media account is a predictor of their default probability. Second, borrowers’ social media presence, such as their social network and social media engagement, are also predictors of default probability. This study proffers new insights for the screening process in P2P lending and novel usage of social media information