ALBERT

Description: Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG)-like feature for SAR ATR (called SAR-HOG). Then, we propose a supervised discriminative dictionary learning (SDDL) method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR), in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC) scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.

Description: Illegal logging, with its related trade of illegally harvested timber, is one of the main environmental and economic problems worldwide. Eliminating illegal timber consumption and production are two practical means to reduce illegal logging. However, the problem of determining which of the two means is more economical remains to be analyzed. In this study, an input–output analysis was conducted to evaluate the consumption and production of illegal timber in different countries. The Global Forest Products Model (GFPM) was employed to analyze the effects of eliminating illegal timber consumption and production on the added value of the forest sector at global and national levels. Results indicated that eliminating illegal timber production is more economical than eliminating consumption at the global level. The former is estimated to decrease the added value of the global forest sector only by 3.37% compared to 7.31% by the latter in 2030. Eliminating the production of illegal timber will result in uneven distribution of social wealth in the forest sector, and will pass the cost of reducing illegal logging onto developing countries. Developed countries would gain more added value and market scale than the global average, whereas developing countries would suffer a loss if illegal timber production is eliminated. Hence, developed countries are encouraged to provide financial support to help developing countries reduce illegal logging.

Description: The nucleation of cubic boron nitride (cBN) single crystals synthesized with lithium nitride (Li3N) as a catalyst under high pressure and high temperature (HPHT) was analyzed. Many nanometer-sized cubic boron nitride nuclei formed in the near surface layer, as detected by high resolution transmission electron microscopy. Based on the experiment results, the transformation kinetics is described by a nucleation and growth process in the thermodynamic stability region of cBN. A theoretical description is developed based on the heterogeneous nucleation and layer growth mechanism, and the relevant parameters are estimated and discussed. The critical crystal radius, r*, increases with the temperature under constant pressure; the change with temperature more pronounced at lower pressure (such as 4.5 GPa). The crystal growth velocity increased with the temperature, and it is parabolic with temperature under certain pressure. These results are consistent with experimental data.

Description: Normally, polarimetric SAR classification is a high-dimensional nonlinear mapping problem. In the realm of pattern recognition, sparse representation is a very efficacious and powerful approach. As classical descriptors of polarimetric SAR, covariance and coherency matrices are Hermitian semidefinite and form a Riemannian manifold. Conventional Euclidean metrics are not suitable for a Riemannian manifold, and hence, normal sparse representation classification cannot be applied to polarimetric SAR directly. This paper proposes a new land cover classification approach for polarimetric SAR. There are two principal novelties in this paper. First, a Stein kernel on a Riemannian manifold instead of Euclidean metrics, combined with sparse representation, is employed for polarimetric SAR land cover classification. This approach is named Stein-sparse representation-based classification (SRC). Second, using simultaneous sparse representation and reasonable assumptions of the correlation of representation among different frequency bands, Stein-SRC is generalized to simultaneous Stein-SRC for multi-frequency polarimetric SAR classification. These classifiers are assessed using polarimetric SAR images from the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) and the Electromagnetics Institute Synthetic Aperture Radar (EMISAR) sensor of the Technical University of Denmark (DTU). Experiments on single-band and multi-band data both show that these approaches acquire more accurate classification results in comparison to many conventional and advanced classifiers.

Description: The expected errors of RADAR sensor networks with linear probabilistic location fingerprints inside buildings with varying Wi-Fi Gaussian strength are discussed. As far as we know, the statistical errors of equal and unequal-weighted RADAR networks have been suggested as a better way to evaluate the behavior of different system parameters and the deployment of reference points (RPs). However, up to now, there is still not enough related work on the relations between the statistical errors, system parameters, number and interval of the RPs, let alone calculating the correlated analytical expressions of concern. Therefore, in response to this compelling problem, under a simple linear distribution model, much attention will be paid to the mathematical relations of the linear expected errors, number of neighbors, number and interval of RPs, parameters in logarithmic attenuation model and variations of radio signal strength (RSS) at the test point (TP) with the purpose of constructing more practical and reliable RADAR location sensor networks (RLSNs) and also guaranteeing the accuracy requirements for the location based services in future ubiquitous context-awareness environments. Moreover, the numerical results and some real experimental evaluations of the error theories addressed in this paper will also be presented for our future extended analysis.

Description: The possibilities of different phase transitions to cBN with Li3N as catalyst at high temperature and high pressure (1600–2200 K, 4.8–6.0 GPa) are analyzed, in the framework of the second law of thermodynamics. The Gibbs free energy (∆G) of three reactions which may happen in the Li3N-BN system: hBN + Li3N→Li3BN2, hBN→cBN, and Li3BN2→cBN + Li3N, is calculated, with the influence of high temperature and high pressure on volume included. We show that ∆G of hBN + Li3N→Li3BN2 and hBN→cBN are between −35~−10 KJ·mol−1 and −25~−19 KJ·mol−1, respectively. However, ∆G of Li3BN2→cBN + Li3N can be positive or negative. The area formed by the positive data is a V-shaped area, which covers the most part of the cBN growing V-shaped area. It confirms that Li3BN2 is stable in the P-T area of cBN synthesis, and cBN is probably transformed directly from hBN. Analysis suggests that Li3BN2 promotes the transition from hBN to cBN.

Description: Grassland biomass is essential for maintaining grassland ecosystems. Moreover, biomass is an important characteristic of grassland. In this study, we combined field sampling with remote sensing data and calculated five vegetation indices (VIs). Using this combined information, we quantified a remote sensing estimation model and estimated biomass in a temperate grassland of northern China. We also explored the dynamic spatio-temporal variation of biomass from 2006 to 2012. Our results indicated that all VIs investigated in the study were strongly correlated with biomass (α 〈 0.01). The precision of the model for estimating biomass based on ground data and remote sensing was greater than 73%. Additionally, the results of our analysis indicated that the annual average biomass was 11.86 million tons and that the average yield was 604.5 kg/ha. The distribution of biomass exhibited substantial spatial heterogeneity, and the biomass decreased from the eastern portion of the study area to the western portion. The interannual biomass exhibited strong fluctuations during 2006–2012, with a coefficient of variation of 26.95%. The coefficient of variation of biomass differed among the grassland types. The highest coefficient of variation was found for the desert steppe, followed by the typical steppe and the meadow steppe.

Description: This paper focuses on suppressing spectral overlap for sub-band spectral estimation, with which we can greatly decrease the computational complexity of existing spectral estimation algorithms, such as nonlinear least squares spectral analysis and non-quadratic regularized sparse representation. Firstly, our study shows that the nominal ability of the high-order analysis filter to suppress spectral overlap is greatly weakened when filtering a finite-length sequence, because many meaningless zeros are used as samples in convolution operations. Next, an extrapolation-based filtering strategy is proposed to produce a series of estimates as the substitutions of the zeros and to recover the suppression ability. Meanwhile, a steady-state Kalman predictor is applied to perform a linearly-optimal extrapolation. Finally, several typical methods for spectral analysis are applied to demonstrate the effectiveness of the proposed strategy.

Description: Energies, Vol. 11, Pages 1155: Research on Double-Fed Induction Generator Low Voltage Ride Through Based on Double Braking Resistors Using Fuzzy Control Energies doi: 10.3390/en11051155 Authors: Hao Dong Hongbin Wu Jing Pan Yu Chen Bin Xu The stator side of a double-fed induction generator (DFIG) is directly connected to the grid, so the DFIG is sensitive to a voltage drop caused by power system faults. A double resistors braking method based on fuzzy control is proposed to improve the performance of low-voltage ride through (LVRT) in this paper. Based on the mathematical model of DFIG, it analyzes the function of a series dynamical braking resistor (SDBR) theoretically. The series impedance value of the SDBR is determined by the variation of the rotor’s open circuit voltage, the voltage and current of the stator and the rotor, and also the heat capacity of the SDBR. In order to improve the LVRT capability of a DFIG under different fault grads, a double series resistors braking mode is presented. Through adopting a fuzzy control strategy, double series resistor switching is implemented. With the example system, the correctness and validity of the proposed method is verified.

Description: IJERPH, Vol. 15, Pages 985: Dose–Response Relationships between Second-Hand Smoke Exposure and Depressive Symptoms among Adolescents in Guangzhou, China International Journal of Environmental Research and Public Health doi: 10.3390/ijerph15050985 Authors: Jingya Huang Bin Xu Dan Guo Ting Jiang Wei Huang Guocong Liu Xiaohua Ye There has been little focus on the possible association between second-hand smoke (SHS) exposure and depressive symptoms among adolescents. Thus, this study aimed to explore the dose–response relationships between SHS exposure and depressive symptoms among adolescents and differentiate these associations in setting-specific exposure and severity-specific outcomes. A cross-sectional study was conducted using a stratified cluster sampling method to obtain a representative sample of high school students in Guangzhou, China. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale. Univariable and multivariable logistic regression models were used to explore the potential associations between SHS exposure and depressive symptoms. Among 3575 nonsmoking students, 29.6% were classified as having probable depressive symptoms and 9.6% had severe depressive symptoms. There were monotonically increasing dose–response relationships between setting-specific (public places, homes, or indoor/outdoor campuses) SHS exposure and severity-specific (probable or severe) depressive symptoms. When examining these relations by source of exposure, we also observed similar dose–response relationships for SHS exposure in campuses from smoking teachers and from smoking classmates. Our findings suggest that regular SHS exposure is associated with a significant, dose-dependent increase in risk of depressive symptoms among adolescents, and highlight the need for smoke-free environments to protect the health of adolescents.