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The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction.

The research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation (“SA-ALC”), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system.

Extensive experiments on two types of Chinese classical historical datasets show that the authors’ solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors’ pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage.

The study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways.

This paper aims to investigate the feasibility of using the combination of Lorentz force and aerodynamic force as a propellantless control method for spacecraft formation.

It is assumed that each spacecraft is equipped with several large flat plates, which can rotate to produce aerodynamic force. Lorentz force can be achieved by modulating spacecraft’s electrostatic charge. An adaptive output feedback controller is designed based on a sliding mode observer to account for unknown uncertainties and the absence of relative velocity measurements. Aiming at distributing the control input, an optimal control allocation method is proposed to calculate the electrostatic charge of the Lorentz spacecraft and control commands for the atmospheric-based actuators.

Numerical examples are provided to demonstrate the effectiveness of the proposed control strategy in the presence of J₂ perturbations. Simulation results show that relative motion in a formation can be precisely controlled by the proposed propellantless control method under uncertainties and unavailability of velocity measurements.

The controllability of the system is not theoretically investigated in the current work.

The proposed control method introduced in this paper can be applied for small satellites formation in low Earth orbit.

The main contribution of the paper is the proposal of the propellantless control approach for satellite formation using the combination of Lorentz force and aerodynamic force, which can eliminate the requirement of the propulsion system.