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There is an increasing demand for higher-accuracy dimensional measurements of nano- and micro-structures. Recently, the authors presented a fiber Bragg grating (FBG) sensor-based dynamic nano-coordinate-measuring machine (CMM) probe for true three-dimensional coordinate measurement, in which a specific mechanical structure with several FBG sensors was developed to provide the probe with sensitivity to loading in all directions.

The study presents a three-dimensional sensing and demodulation system based on an improved matched filter design and the time division multiplexing technique that helps solve the problem of multiplex FBG-signals conflicts. In addition, the application of the dynamic mode of the probe system effectively solves the problem presented by the surface interaction forces.

Consequently, this FBG-based vibrating probe system has increased sensitivity to strain, while maintaining smaller contact force. The experiments for testing probe performance show that the prototype yielded a measurement resolution of 13 nm, a repeatability of 50 nm and a vertical measurement force of less than1.5 mN.

The force tests in the horizontal directions are difficult to conduct because both the probe and the dynamometer are only adaptable to vertical use.

Development of the FBG-based dynamic nano-coordinate-measuring machine probe will achieve a new and inexpensive method for higher-accuracy dimensional measurements of nano- and micro-structures, such as micro-electromechanical systems, micro-fluidic chips, inkjet and diesel engine injector nozzles that are in overall dimensions within the micrometer scale.

The study presents a three-dimensional sensing and demodulation system for the vibrating nano-coordinate-measuring machine probe based on FBG sensors. The prototype yielded a measurement resolution of 13 nm, a repeatability of 50 nm and a vertical measurement force of less than1.5 mN.

Sentiment identification of Chinese text faces many challenges, such as requiring complex preprocessing steps, preparing various word dictionaries carefully and dealing with a lot of informal expressions, which lead to high computational complexity.

A method based on Chinese characters instead of words is proposed. This method represents the text into a fixed length vector and introduces the chi-square statistic to measure the categorical sentiment score of a Chinese character. Based on these, the sentiment identification could be accomplished through four main steps.

Experiments on corpus with various themes indicate that the performance of proposed method is a little bit worse than existing Chinese words-based methods on most texts, but with improved performance on short and informal texts. Especially, the computation complexity of the proposed method is far better than words-based methods.

The proposed method exploits the property of Chinese characters being a linguistic unit with semantic information. Contrasting to word-based methods, the computational efficiency of this method is significantly improved at slight loss of accuracy. It is more sententious and cuts off the problems resulted from preparing predefined dictionaries and various data preprocessing.