Search

Monitoring of CO2 column concentrations is valuable for atmospheric research. A mobile open-path system was developed based on tunable diode laser absorption spectroscopy (TDLAS) to measure atmospheric CO2 column concentrations. A laser beam was emitted downward from a distributed feedback diode laser at 2 μm and then reflected by the retroreflector array on the ground. We measured the CO2 column concentrations over the 20 and 110 m long vertical path. Several single-point sensors were distributed at different heights to provide comparative measurements for the open-path TDLAS system. The results showed that the minimum detection limit of system was 0.52 ppm. Some similarities were observed in trends from the open-path TDLAS system and these sensors, but the average of these sensors was more consistent with the open-path TDLAS system values than the single-point measurement. These field measurements demonstrate the feasibility of open-path TDLAS for measuring the CO2 column concentration and monitoring carbon emission over large areas. [ABSTRACT FROM AUTHOR]

Calibration/Validation (Cal/Val) of satellite altimeters is fundamental for monitoring onboard sensor performance and ensuring long-term data quality. As altimeter technology has been evolving rapidly from profile to wide swath and interferometric altimetry, different requirements regarding Cal/Val have emerged. Most current Cal/Val technology has been developed for conventional profile altimeters, whereby satellite observations are compared against measurements at one point along orbit lines. However, the application of this type of Cal/Val technique to swath interferometric altimeters with two-dimensional measurements is difficult. Here, we propose a new strategy for the evaluation of interferometric altimeters based on comparison of wave-induced sea surface elevation (WSSE) spectra from one- and two-dimensional measurements. This method assumes that the WSSE variance of an equilibrium wave field is uniform and can be measured equivalently in the space or time domains. The method was first tested with simulated data and then used to evaluate the performance of an airborne interferometric radar altimeter system (AIRAS) using Global Navigation Satellite System (GNSS) buoy measurements. The differences between the WSSE variances from the AIRAS and two GNSS buoys were below 8 cm2, corresponding to a standard deviation of 2.8 cm, which could serve as a reference for the WSSE error over the scale range of waves. The correlation coefficient between the AIRAS and GNSS buoys was approximately 0.90, indicating that the error was small relative to the WSSE signals. In addition, the sea surface height (SSH) difference measured by the AIRAS was compared with that derived from the GNSS buoys at two sites. The results indicated that the error of the SSH difference was 3 cm. This approach represents a possible technique for the Cal/Val of future spaceborne/airborne interferometric altimeters; however, additional experiments and applications are needed to verify the feasibility of this method. [ABSTRACT FROM AUTHOR]

Manufacturing industry accounts for about one-third of the world's total energy consumption (TEC). This study aims to develop a novel mixed-integer mathematical model to represent the direct energy consumption of machines and indirect energy consumption on a shop floor. In comparison with traditional modeling methods, this paper proposes an effective gene expression programming-based rule mining (GEP-RM) algorithm to generate dispatching rules automatically. This method consists of three attributes that have significant impacts on the TEC of a manufacturing process. In addition, diversified rule mining operators with self-learning are designed to ensure population diversity and convergence. Moreover, a perturbation trigger mechanism for reconstructing rules is introduced to avoid being trapped into a local optimum. An unsupervised learning algorithm is achieved by setting the evolution direction with global best and current worst in order to mine the value of the substantial historical data. Experimental results have shown that the proposed multi-attribute rule mining approach outperforms other dispatching rules in terms of energy saving and production efficiency. [ABSTRACT FROM AUTHOR]