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The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.

The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle. Based on data mechanism models, it predicts the lifespan of key components, evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.

The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system, which helps operators to monitor the operation of vehicle online, predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.

This system improves the efficiency of rail vehicle operation, scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

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.

The purpose of this paper is to provide a historical review of China’s anti-corruption efforts, from the ancient period of Chinese slavery societies to the late 1970s before China launched its profound economic reform, under the current status of the harsh crusade against corruption that the Chinese new leadership initiated.

This paper is mainly based on a great deal of historical literature and empirical findings, with relevant comparative analysis on policies and regulations between various periods of China.

The phenomenon of corruption has existed in Chinese history for thousands of years, throughout Chinese slavery societies, feudal societies, republic period and the People’s Republic of China (PRC). Anti-corruption laws formed an important part of ancient Chinese legal system, and each dynasty has made continuous and commendable progress on fighting such misconduct. Innumerable initiatives have also been taken by the ruling party Chinese Communist Party (CCP) since the founding of the PRC. The PRC government created various specially designed government organizations and a series of updated regulations for preventing economic crimes. They have realized that periodic movements against corruption would no longer be helpful, and the paramount issue nowadays is indeed how bold the leaders are in striking out those unhealthy tendencies.

This paper fills in the blanks in the Western world with a comprehensive description of, and comments on, the historical efforts on China’s corruption and economic crime prevention. It also, in various ways, provides meaningful information that links to China’s current furious war against corruption.