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As a type of angular displacement sensor, the Hall-effect magnetic encoder incorporates many advantages. While compared with the photoelectric encoder, the magnetic encoder nevertheless has lower precision and lower resolution. So, the purpose of this essay is to find a way to increase the precision and resolution of the magnetic encoder.

By combining a single-pole magnetic encoder and a multi-pole AlNiCo magnet, the precision and resolution of this combined magnetic encoder are increased without increasing its volume or complicating its structure. A special algorithm system is developed to ensure faithful encoding and decoding.

Tests show that the combined magnetic encoder, with a diameter of 67.12 mm (including shaft) and thickness of 6.9 mm, has a precision of ±6′, compared with a 15-bit photoelectric encoder and a static resolution of ±0.6′.

This new kind of magnetic encoder could be used in specialized fields which need high-precision servo-control systems that are small, have ultra-low-speed and high-speed ratios and are non-oil-polluting or shock-resistant.

In the era of Big Data, network digital resources are growing rapidly, especially the short-text resources, such as tweets, comments, messages and so on, are showing a vigorous vitality. This study aims to compare the categories discriminative capacity (CDC) of Chinese language fragments with different granularities and to explore and verify feasibility, rationality and effectiveness of the low-granularity feature, such as Chinese characters in Chinese short-text classification (CSTC).

This study takes discipline classification of journal articles from CSSCI as a simulation environment. On the basis of sorting out the distribution rules of classification features with various granularities, including keywords, terms and characters, the classification effects accessed by the SVM algorithm are comprehensively compared and evaluated from three angles of using the same experiment samples, testing before and after feature optimization, and introducing external data.

The granularity of a classification feature has an important impact on CSTC. In general, the larger the granularity is, the better the classification result is, and vice versa. However, a low-granularity feature is also feasible, and its CDC could be improved by reasonable weight setting, even exceeding a high-granularity feature if synthetically considering classification precision, computational complexity and text coverage.

This is the first study to propose that Chinese characters are more suitable as descriptive features in CSTC than terms and keywords and to demonstrate that CDC of Chinese character features could be strengthened by mixing frequency and position as weight.

The purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear.

In this paper, genetic programming (GP), which is originally used to solve the symbolic regression problem, is used to build the regression model related to tool wear with the strong regression ability. GP is improved in genetic operation and weighted matrix. The performance of GP is verified in the tool vibration, force and acoustic emission data provided by 2010 prognostics health management.

In result, the regression model discovered by GP can identify the state of tool wear. Compared to other regression algorithms, e.g. support vector regression and polynomial regression, the identification of GP is more precise.

The regression models built in this paper can only make an assessment of the current wear state with current signals of tool. It cannot predict or estimate the tool wear after the current state. In addition, the generalization of model has some limitations. The performance of models is just proved in the signals from the same type of tools and under the same work condition, and different tools and different work conditions may have influences on the performance of models.

In this study, the discovered regression model can identify the state of tool wear precisely, and the identification performances of model applied in other tools are also excellent. It can provide a significant information about the health of tool, so the tools can be replaced or repaired in time, and the loss caused by tool damage can be avoided.

The purpose of this paper is to calculate the bank efficiency of removing potential risks that are hidden from the extreme portfolio of bank’s assets and further compare the differences and causes of bank’s efficiency and potential risk level between China’s representative banks and OECD representative banks in 2011-2015.

Based on the weight-limited DEA model, this paper calculates the bank’s efficiency and further compares the differences between China’s representative banks and OECD representative banks by using commercial banks’ transnational data.

By analyzing US representative banks’ data, the authors find that the excessive expansion of the scale of banks’ investment for the non-real economy shrinks after the bubble burst and would not improve the efficiency of banks immediately. The OECD representative banks rather prefer to extreme asset portfolio so that the potential risks gradually increase, while there is a diminishing effect on investments in non-real economies to improve bank efficiency. On the other hand, China’s representative banks have the signs of reducing investment in the real estate market, but the existence of the bubble in the market led to a lagged effect on the impact of adjustment of bank asset portfolio on efficiency.

This paper has practical significance for commercial banks to improve efficiency and reduce credit risks. This is conducive to the implementation of targeted supervision by the banking supervision department.

Based on the lesson that the financial crisis created by the real estate bubble burst in the USA in 2008 and the financial market active guidance of the developed economies, faced with the reality of Chinese real estate market bubble rising and the continuous improvement of Chinese financial market, this paper compares the differences between representative banks in China and OECD, and explores the causes by using the cross-country data of commercial banks.

By adjusting the weight of the input variables in the efficiency measurement, quantifying the risk is often overlooked by the changes in bank efficiency. This potential risk is caused by the bank’s investment preferences in the non-real economy represented by real estate and tradable financial assets.

The purpose of this paper is to investigate the effects of creator credibility and backer endorsement on the levels of donation to crowdfunding campaigns in a focal muslim community and to determine the success factors of projects in developing and developed countries.

This research adopts a quantitative approach, utilizing 223 crowdfunding projects from LaunchGood to examine the expected connections. The crowdfunding projects in the distinctive data set (Ramadan) are similar, permitting exploration of the role of creator credibility and backer endorsement in a unique context of religious-based crowdfunding activity.

Creators’ credibility and backers’ endorsements are decisive factors in crowdfunding success. Results from an additional test related to location (developing and developed countries) suggest that creators’ credibility is more important than backers’ endorsements, especially in developing countries.

The projects used in the data set are homogenous and take place in a specific context. The limited number of available observations influences the testing function and capacity of the models and variables. Further, the investigation is related to a specific community, so might not be applicable to all communities.

Potential supporters from the community are vital for the success of crowdfunding projects. This study informs campaign efforts in a religious community by stressing the importance of creators’ credibility and backers’ endorsements. The study also sheds light on factors that affect crowdfunding success in developing and developed countries.

This study helps to explain successful crowdfunding practices in donation-based crowdfunding projects within a religious community context. Results improve scholars’ ability to comprehend individuals’ supporting behavior, providing empirical evidence of creators’ credibility and backers’ endorsement on project success. The study also addresses the determinants of crowdfunding success in developing and developed countries.

The purpose of this study was to conduct a comprehensive analysis of the impact and its mechanism on the transfer of agricultural labor forces in the surrounding areas resulting from the establishment of a natural reserve, which holds great significance. The significance of this analysis is on the ecological protection of the natural reserve and the coordinated development of local social economy.

This study first performs an analysis on the impact and its mechanism on the establishment of the natural reserve on the transfer of agricultural labor forces from two aspects, which are push and pull factors. Then, based on county panel data in Jiangxi Province from 1995 to 2012, this study builds a generalized difference-in-difference model and performs an empirical study on the impact, heterogeneity and its mechanism on the establishment of the natural reserve on the transfer of agricultural labor forces.

The empirical analysis reveals that the establishment of natural reserves would significantly promote the transfer of agricultural labor forces to non-agricultural sectors. The robust test and placebo test with changed estimation methods verify the robust of the result. The result passes the parallel trend test and shows that the impact is most significant within one year after the implementation of the policy. From the mechanism analysis, the impact mainly comes from the “push” effect brought by the restricted development of agricultural production and primary industry on agricultural labor forces, and the “pull” effect brought by the development of local tertiary industry.

The conclusion of this study enriches the understanding of the internal mechanism between the establishment of natural reserves and the transfer of agricultural labor forces from the push and pull factors, and can provide reference for formulating policies to promote the coordinated development of natural reserve construction and regional social economy.

This paper aims to investigate the equivalent relationship between accelerated corrosion tests and real environmental spectrum of suspenders in long-span suspension bridge considering multiple factors action.

Based on Faraday's law, corrosion current was used as a measure of metal corrosion, and the equivalent conversion relationship between laboratory environment and real service environment was established. The equivalent conversion method for bridge structural steel had been determined under different temperature, humidity, pH value and NaCl concentration conditions. The compilation of environmental spectra for large span bridges considering multiple factors and the principle of equivalent conversion have been proposed.

Environmental factors, including temperature, humidity, pH value and NaCl concentration, have significant impact on the corrosion degree of suspension steel wires, and only considering these two factors for equivalent conversion cannot accurately reflect the true service environment of the bridge. The 33.8-h salt spray accelerated corrosion test using the standard conditions can be equivalent to one year of suspenders corrosion in a real service environment.

The equivalent accelerated corrosion method for steel wires proposed in this study can effectively predict the corrosion degree of the suspenders, which has been verified to be correct and can provide theoretical guidance for the development of corrosion test plans for steel wires and engineering technical basis for anti-corrosion control and calendar life research of suspension bridge suspenders.

Limited geometric accuracy is one of the major challenges that hinder the wider application of additive manufacturing (AM). This paper aims to predict in-plane shape deviation for online inspection and compensation to prevent error accumulation and improve shape fidelity in AM.

A sequence-to-sequence model with an attention mechanism (Seq2Seq+Attention) is proposed and implemented to predict subsequent layers or the occluded toolpath deviations after the multiresolution alignment. A shape compensation plan can be performed for the large deviation predicted.

The proposed Seq2Seq+Attention model is able to provide consistent prediction accuracy. The compensation plan proposed based on the predicted deviation can significantly improve the printing fidelity for those layers detected with large deviations.

Based on the experiments conducted on the knee joint samples, the proposed method outperforms the other three machine learning methods for both subsequent layer and occluded toolpath deviation prediction.

This work fills a research gap for predicting in-plane deviation not only for subsequent layers but also for occluded paths due to the missing scanning measurements. It is also combined with the multiresolution alignment and change point detection to determine the necessity of a compensation plan with updated G-code.

This study aims to reveal the room-temperature effect of a superconducting gravimeter prototype, which will guide its subsequent optimization to improve its gravimetric measurement accuracy.

Without leveling, the prototype output signal, tilt data and room temperature were measured under steady operating conditions. After analyzing the correlations of the three data sets, the residuals of the prototype’s output signal were compensated using the tilt data and the geodynamic effects (ocean tide loading, atmospheric loading and the gravitational effect of polar motion) were then corrected.

The remaining residuals after correction may be caused by small tilt variations that are due to the sensor chamber temperature and radiation shield temperature changes. These small tilt variations were submerged in the tilt signal noise. Although the peak-to-peak noise of the tiltmeter does not exceed 15 µrad, it can still produce gravimetric deviations above 60 µGal when the prototype is significantly tilted.

This study analyzes in detail the room-temperature effect of a superconducting gravimeter prototype, introduces the tilt effect of the relative gravimeters to compensate for the gravimetric deviations and emphasizes that the improvement of fine leveling and the accuracy of the tiltmeter are key requirements for the prototype to perform high-accuracy gravity measurement tasks.