ALBERT

Description: Alpine swamp meadow on the Tibetan Plateau is among the most sensitive areas to climate change. Accurate quantification of the GPP in alpine swamp meadow can benefit our understanding of the global carbon cycle. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) products (GPP_MOD) provide a pathway to estimate GPP in this remote ecosystem. However, the accuracy of the GPP_MOD estimation in this representative alpine swamp meadow is still unknown. Here five years GPP_MOD was validated using GPP derived from the eddy covariance flux measurements (GPP_EC) from 2009 to 2013. Our results indicated that the GPP_EC was strongly underestimated by GPP_MOD with a daily mean less than 40% of EC measurements. To reduce this error, the ground meteorological and vegetation leaf area index (LAIG) measurements were used to revise the key inputs, the maximum light use efficiency (εmax) and the fractional photosynthetically active radiation (FPARM) in the MOD17 algorithm. Using two approaches to determine the site-specific εmax value, we suggested that the suitable εmax was about 1.61 g C MJ−1 for this alpine swamp meadow which was considerably larger than the default 0.68 g C MJ−1 for grassland. The FPARM underestimated 22.2% of the actual FPAR (FPARG) simulated from the LAIG during the whole study period. Model comparisons showed that the large inaccuracies of GPP_MOD were mainly caused by the underestimation of the εmax and followed by that of the undervalued FPAR. However, the DAO meteorology data in the MOD17 algorithm did not exert a significant affection in the MODIS GPP underestimations. Therefore, site-specific optimized parameters inputs, especially the εmax and FPARG, are necessary to improve the performance of the MOD17 algorithm in GPP estimation, in which the calibrated MOD17A2 algorithm (GPP_MODR3) could explain 91.6% of GPP_EC variance for the alpine swamp meadow.

Description: Integrating the advantage of magnetic bearings with a double gimble control moment gyroscope (DGCMG), a magnetically suspended DGCMG (MSDGCMG) is an ideal actuator in high-precision, long life, and rapid maneuver attitude control systems. The work presented here mainly focuses on performance testing of a MSDGCMG independently developed by Beihang University, based on the single axis air bearing table. In this paper, taking into sufficient consideration to the moving-gimbal effects and the response bandwidth limit of the gimbal, a special MSDGCMG steering law is proposed subject to the limits of gimbal angle rate and angle acceleration. Finally, multiple experiments are carried out, with different MSDGCMG angular momenta as well as different desired attitude angles. The experimental results indicate that the MSDGCMG has a good gimbal angle rate and output torque tracking capabilities, and that the attitude stability with MSDGCMG as actuator is superior to 10−3°/s. The MSDGCMG performance testing in this paper, carried out under moving-base condition, will offer a technique base for the future research and application of MSDGCMGs.

Description: With the rapid development of wind energy, relay protection for large-scale wind farms has been attracting some researchers, due to the absence of standards. Based on the large-scale doubly fed induction generator (DFIG)-based wind farms located in Gansu Province, China, this paper studies the differential protection for the outgoing power transformer of large-scale DFIG-based wind farms. According to the equivalent circuit of the power grid integrated with wind farms, the main frequency components of current and voltage during faults are identified mathematically and then verified by simulations. The results show that the frequencies of current and voltage at the terminals of outgoing transmission lines are inconsistent. Following the feature of frequency inconsistency, the adaptability of differential protection is analyzed, and it is found that differential protection for an outgoing transformer in large-scale wind farms may fail once ignoring the frequency inconsistency. Simulation studies demonstrate that inconsistent frequency characteristics will deteriorate the sensitivity and reliability of differential protection. Finally, several suggestions are provided for improving the performance of relay protections for large-scale DFIG-based wind farms.

Description: Traffic congestion clustering judgment is a fundamental problem in the study of traffic jam warning. However, it is not satisfactory to judge traffic congestion degrees using only vehicle speed. In this paper, we collect traffic flow information with three properties (traffic flow velocity, traffic flow density and traffic volume) of urban trunk roads, which is used to judge the traffic congestion degree. We first define a grey relational clustering model by leveraging grey relational analysis and rough set theory to mine relationships of multidimensional-attribute information. Then, we propose a grey relational membership degree rank clustering algorithm (GMRC) to discriminant clustering priority and further analyze the urban traffic congestion degree. Our experimental results show that the average accuracy of the GMRC algorithm is 24.9% greater than that of the K-means algorithm and 30.8% greater than that of the Fuzzy C-Means (FCM) algorithm. Furthermore, we find that our method can be more conducive to dynamic traffic warnings.

Description: Minerals, Vol. 8, Pages 297: Mineralogical and Geochemical Characteristics of Trace Elements in the Yongdingzhuang Mine, Datong Coalfield, Shanxi Province, China Minerals doi: 10.3390/min8070297 Authors: Yue Yuan Shuheng Tang Songhang Zhang Ning Yang Fifteen samples of No. 4 coal from the Yongdingzhuang Mine in Datong Coalfield were tested for their elemental compositions, modes of occurrence, and mineralogical compositions, using X-ray powder diffraction, X-ray fluorescence spectrometry, inductively coupled plasma mass spectrometry, and scanning electron microscopy equipped with an energy-dispersive X-ray spectrometer. The samples have low sulfur content (0.63%). The major minerals are kaolinite and quartz, followed by pyrite and anatase. Compared with averages for the Chinese coals, the percentages of SiO2 (15.11%), TiO2 (0.7%), and Al2O3 (10.39%) are much higher. In No. 4 coals, Li (62.81 μg/g), Be (6.94 μg/g), Zr (235 μg/g), Ga (17.04 μg/g), F (165.53 μg/g), Tl (1.93 μg/g), and Hg (0.34 μg/g) are some potentially valuable and toxic trace elements with higher concentrations than Chinese coals and World hard coals. Lithium and F mainly have kaolinite associations. With the exception of kaolinite, Li, and F also partly occur in anatase, gorceixite and goyazite. Beryllium largely occurs in anatase; gallium is mainly associated with kaolinite and to a lesser extent, with gorceixite and goyazite; zirconium is associated with kaolinite, gorceixite and goyazite; and thallium and Hg occur in in pyrite. Potentially valuable elements (including Al, Li, Ga, and Zr) might be recovered as value-added byproducts from coal ash. Toxic elements (e.g., Be, F, Tl, and Hg) might have potential adverse effects to the environment and human health during coal processing. In addition, the distribution patterns of rare earth elements and yttrium (REY) indicate that the REY in No. 4 coals originated from the granite of Yinshan Oldland, and natural waters or hydrothermal solutions that may circulate in coal basins.

Description: Alpine meadow ecosystem is among the highest soil carbon density and the most sensitive ecosystem to climate change. Partitioning autotrophic (Ra) and heterotrophic components (Rm) of ecosystem respiration (Re) is critical to evaluating climate change effects on ecosystem carbon cycling. Here we introduce a satellite-based method, combining MODerate resolution Imaging Spectroradiometer (MODIS) products, eddy covariance (EC) and chamber-based Re components measurements, for estimating carbon dynamics and partitioning of Re from 2009 to 2011 in a typical alpine meadow on the Tibetan Plateau. Six satellite-based gross primary production (GPP) models were employed and compared with GPP_EC, all of which appeared to well explain the temporal GPP_EC trends. However, MODIS versions 6 GPP product (GPP_MOD) and GPP estimation from vegetation photosynthesis model (GPP_VPM) provided the most reliable GPP estimation magnitudes with less than 10% of relative predictive error (RPE) compared to GPP_EC. Thus, they together with MODIS products and GPP_EC were used to estimate Re using the satellite-based method. All satellite-based Re estimations generated an alternative estimation of Re_EC with negligible root mean square errors (RMSEs, g C m−2 day−1) either in the growing season (0.12) or not (0.08). Moreover, chamber-based Re measurements showed that autotrophic contributions to Re (Ra/Re) could be effectively reflected by all these three satellite-based Re partitions. Results showed that the Ra contribution of Re were 27% (10–48%), 43% (22–59%) and 56% (33–76%) from 2009 to 2011, respectively, of which inter-annual variation is mainly attributed to soil water dynamics. This study showed annual temperature sensitivity of Ra (Q10,Ra) with an average of 5.20 was significantly higher than that of Q10,Rm (1.50), and also the inter-annual variation of Q10,Ra (4.14–7.31) was larger than Q10,Rm (1.42–1.60). Therefore, our results suggest that the response of Ra to temperature change is stronger than that of Rm in this alpine meadow.

Description: Energies, Vol. 10, Pages 1903: A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization Energies doi: 10.3390/en10111903 Authors: Xiyun Yang Guo Fu Yanfeng Zhang Ning Kang Feng Gao Intermittency and uncertainty pose great challenges to the large-scale integration of wind power, so research on the probabilistic interval forecasting of wind power is becoming more and more important for power system planning and operation. In this paper, a Naive Bayesian wind power prediction interval model, combining rough set (RS) theory and particle swarm optimization (PSO), is proposed to further improve wind power prediction performance. First, in the designed prediction interval model, the input variables are identified based on attribute significance using rough set theory. Next, the Naive Bayesian Classifier (NBC) is established to obtain the prediction power class. Finally, the upper and lower output weights of NBC are optimized segmentally by PSO, and are used to calculate the upper and lower bounds of the optimal prediction intervals. The superiority of the proposed approach is demonstrated by comparison with a Naive Bayesian model with fixed output weight, and a rough set-Naive Bayesian model with fixed output weight. It is shown that the proposed rough set-Naive Bayesian-particle swarm optimization method has higher coverage of the probabilistic prediction intervals and a narrower average bandwidth under different confidence levels.

Description: Remote Sensing, Vol. 10, Pages 360: Assessing the Defoliation of Pine Forests in a Long Time-Series and Spatiotemporal Prediction of the Defoliation Using Landsat Data Remote Sensing doi: 10.3390/rs10030360 Authors: Chenghao Zhu Xiaoli Zhang Ning Zhang Mohammed Hassan Lin Zhao Pine forests (Pinus tabulaeformis) have been in danger of defoliation by a caterpillar in the west Liaoning province of China for more than thirty years. This paper aims to assess and predict the degree of damage to pine forests by using remote sensing and ancillary data. Through regression analysis of the pine foliage remaining ratios of field plots with several vegetation indexes of Landsat data, a feasible inversion model was obtained to detect the degree of damage using the Normalized Difference Infrared Index of 5th band (NDII5). After comparing the inversion result of the degree of damage to the pine in 29 years and the historical damage record, quantized results of damage assessment in a long time-series were accurately obtained. Based on the correlation analysis between meteorological variables and the degree of damage from 1984 to 2015, the average degree of damage was predicted in temporal scale. By adding topographic and other variables, a linear prediction model in spatiotemporal scale was constructed. The spatiotemporal model was based on 5015 public pine points for 24 years and reached 0.6169 in the correlation coefficient. This paper provided a feasible and quantitative method in the spatiotemporal prediction of forest pest occurrence by remote sensing.