No abstract

We demonstrate a tunable external cavity tapered amplifier laser (ECTAL) using a narrowband interference filter as the wavelength discriminator. The laser is tunable over a wavelength range from 1006 to 1031 nm with an output power of ∼1  W. The amplified stimulated emission of the laser system is suppressed to better than 32 dB. The laser is applied to study the saturation spectroscopy on the R(39) 57-0 line of iodine molecule, which, to our best knowledge, is the first measurement of this line close to the dissociation limit. The linewidth of the a component is ∼2  MHz at the iodine vapor pressure of ∼11  Pa, and the pressure-broadening coefficient is ∼156  kHz/Pa. This laser system is also used for the injection seeding of a 1030 nm disk laser to perform hyperfine spectroscopy of muonic hydrogen. To reach a satisfactory condition for disk laser use, the ECTAL is successfully stabilized to the iodine Doppler-free spectroscopy of the P(26) 43-0 line near 515 nm, with continuous locking over 48 h.

In the current era of data science, data quality has a significant and critical impact on business operations. This is no different for the meteorological data encountered in the field of meteorology. However, the conventional methods of meteorological data quality control mainly focus on error detection and null-value detection; that is, they only consider the results of the data output but ignore the quality problems that may also arise in the workflow. To rectify this issue, this paper proposes the Total Meteorological Data Quality (TMDQ) framework based on the Total Quality Management (TQM) perspective, especially considering the systematic nature of data warehousing and process focus needs. In practical applications, this paper uses the proposed framework as the basis for the development of a system to help meteorological observers improve and maintain the quality of meteorological data in a timely and efficient manner. To verify the feasibility of the proposed framework and demonstrate its capabilities and usage, it was implemented in the Tamsui Meteorological Observatory (TMO) in Taiwan. The four quality dimension indicators established through the proposed framework will help meteorological observers grasp the various characteristics of meteorological data from different aspects. The application and research limitations of the proposed framework are discussed and possible directions for future research are presented.

With the rise of Industry 4.0 and artificial intelligence, the demand for industrial automation and precise control has increased. Machine learning can reduce the cost of machine parameter tuning and improve high-precision positioning motion. In this study, a visual image recognition system was used to observe the displacement of an XXY planar platform. Ball-screw clearance, backlash, nonlinear frictional force, and other factors affect the accuracy and reproducibility of positioning. Therefore, the actual positioning error was determined by inputting images captured by a charge-coupled device camera into a reinforcement Q-learning algorithm. Time-differential learning and accumulated rewards were used to perform Q-value iteration to enable optimal platform positioning. A deep Q-network model was constructed and trained through reinforcement learning for effectively estimating the XXY platform’s positioning error and predicting the command compensation according to the error history. The constructed model was validated through simulations. The adopted methodology can be extended to other control applications based on the interaction between feedback measurement and artificial intelligence.