Z-domain modeling of peak current mode control for full-bridge DC-DC buck converters

Traditional local-averaged state-space modeling for peak current mode (PCM) controls fails to explain the subharmonic oscillation phenomenon when the spectrum is higher than half of the switching frequency. To address this problem, this paper presents a small-signal modeling method in the z-domain, and builds a discrete linear model for the current loop of a full-bridge DC-DC converter. This discrete model is converted into a second-order continuous model that is able to represent the system performance with a wider frequency range. A frequency-domain analysis shows that this model can be used to explain the subharmonic oscillations and unstable characteristics. This provides an engineering guideline for the practical design of slope compensation. The effectiveness of the proposed modeling method has been verified by simulation and experimental results with a prototype working in the Buck mode

Synthesis and characterization of 2-(2-benzhydrylnaphthyliminomethyl)pyridylnickel halides: formation of branched polyethylene

A series of 2-(2-benzhydrylnaphthyliminomethyl)pyridine derivatives (L1–L3) was prepared and used to synthesize the corresponding bis-ligated nickel(II) halide complexes (Ni1–Ni6) in good yield. The molecular structures of representative complexes, namely the bromide Ni3 and the chloride complex Ni6, were confirmed by single crystal X-ray diffraction, and revealed a distorted octahedral geometry at nickel. Upon activation with either methylaluminoxane (MAO) or modified methylaluminoxane (MMAO), all nickel complex pre-catalysts exhibited high activities (up to 2.02 × 10⁷ g(PE) mol⁻¹(Ni) h⁻¹) towards ethylene polymerization, producing branched polyethylene of low molecular weight and narrow polydispersity. The influence of the reaction parameters and the nature of the ligands on the catalytic behavior of the title nickel complexes were investigated

A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognition

Electroencephalography (EEG)-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM) is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects). Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR) can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE), which achieves values of 77.88% and 7.33% on average, respectively. For the online analysis, the average classification accuracy and standard deviation of ASFM in the subject-to-subject evaluation for all the 15 subjects in a dataset was 75.11% and 7.65%, respectively, gaining a significant performance improvement compared to the best baseline LR which achieves 56.38% and 7.48%, respectively. The experimental results confirm the effectiveness of the proposed method relative to state-of-the-art methods. Moreover, computational efficiency of the proposed ASFM method is much better than standard domain adaptation; if the numbers of training samples and test samples are controlled within certain range, it is suitable for real-time classification. It can be concluded that ASFM is a useful and effective tool for decreasing domain discrepancy and reducing performance degradation across subjects and sessions in the field of EEG-based emotion recognition

Petrogenesis and tectonic setting of the quartz porphyry in Mazhuangshan gold deposit, Eastern Tianshan Orogen: Evidence from geochemistry, zircon U-Pb geochronology and Sr-Nd-Hf isotopes

The Mazhuangshan gold deposit is located in the eastern part of the Eastern Tianshan Orogen, where abundant quartz porphyries are widely outcropped. The quartz porphyries are EW- or NW-trending, distributing in a narrow and long belt and intruding along the fault in middle part of the mining area. It is grey-write coloured and exhibits a porphyritic texture. Quartz, K-feldspar and plagioclase are embedded in a fine-grained groundmass, which mainly comprises felsic minerals and minor dark minerals. The (Na2O + K2O) contents of quartz porphyry samples are 3.55%similar to 9.67%, the Al2O3 contents are 10.37%similar to 14.28%, with the K2O/Na2O ratio values of 2.57 similar to 66.5, which show the peraluminous characteristics. The quartz porphyries have medium Sigma REE contents (65 x 10(-6)similar to 161 x 10(-6)) and medium REE fractionation ([La/Yb](N) = 6.15 similar to 12.5), with apparently enrichment of light rare earth and slightly negative Eu anomaly. The subvolcanic rocks are riched in Rb, K, Th, U, Pb elements and depleted in Sr, Ba, P, Nb and Ti elements, which is similar to the characteristic of the rocks formed in active continental margin. Quartz porphyry samples in the Mazhuangshan area show negative correlation between P2O5 and SiO2 elements, positive correlation between Y vs. Rb elements and Th vs. Rb values, which are consistent with the typical I-type granite trends. The zircons from quartz porphyries in Mazhuangshan gold deposit yielded U-Pb concordant age of 315.4 +/- 0.6Ma (MSWD=0.67) and weighted mean zircon Pb-206/U-238 age of 316.0 +/- 2.0Ma (MSWD = 0.23), which shows that quartz porphyry intruded at 314 similar to 318Ma. Sr-Nd-Hf isotopic compositions indicate that quartz porphyry is characterized by high (Sr-87/Sr-86), values (0.7077 similar to 0.7102), low epsilon(Nd) (t) values (-1.62 similar to 1.82), with relatively young model ages (t(DM2)) ranging from 0. 9 to 1.2Ga. The epsilon(Hf) (t) values of zircons are -3.2 similar to 0.4 and the corresponding t(DM2) model ages are 1.28 similar to 1.51Ga. These features illustrate that the quartz porphyry magmas are derived from the parial melting of the lower crustal materials that involved some mantle component. Comprehensive geological, geochemical and chronological studies demonstrate that the eastern part of the Eastern Tianshan was in an active continental margin arc environment related to subduction during the Late Carboniferous. Quartz porphyry may be the product of the subduction of oceanic crust during the Late Carboniferous, which resulted in partial melting of the new lower crust that involved some mantle component and finally emplaced in the active continental margin arc environment

A new specular reflection optimization algorithm

With the development of optimization theory and the application of computer technology, some new intellective optimization algorithms are developed quickly and applied widely, which is becoming the most important method for optimization problems. In this article, a new intellective optimization algorithm—Specular Reflection Algorithm—is proposed by authors who are inspired by the physical function of mirror. The traditional mathematical theory is used to prove the global convergence of this new algorithm. In order to validate the performance of this algorithm, three classical testing functions are adopted; then the algorithm is used to solve discrete engineering optimization problems and the results indicate that the Specular Reflection Algorithm has higher computation efficiency and extensive prospect for engineering application

Dictionary learning based noisy image super-resolution via distance penalty weight model

<div><p>In this study, we address the problem of noisy image super-resolution. Noisy low resolution (LR) image is always obtained in applications, while most of the existing algorithms assume that the LR image is noise-free. As to this situation, we present an algorithm for noisy image super-resolution which can achieve simultaneously image super-resolution and denoising. And in the training stage of our method, LR example images are noise-free. For different input LR images, even if the noise variance varies, the dictionary pair does not need to be retrained. For the input LR image patch, the corresponding high resolution (HR) image patch is reconstructed through weighted average of similar HR example patches. To reduce computational cost, we use the atoms of learned sparse dictionary as the examples instead of original example patches. We proposed a distance penalty model for calculating the weight, which can complete a second selection on similar atoms at the same time. Moreover, LR example patches removed mean pixel value are also used to learn dictionary rather than just their gradient features. Based on this, we can reconstruct initial estimated HR image and denoised LR image. Combined with iterative back projection, the two reconstructed images are applied to obtain final estimated HR image. We validate our algorithm on natural images and compared with the previously reported algorithms. Experimental results show that our proposed method performs better noise robustness.</p></div

Distribution Adaptation and Classification Framework Based on Multiple Kernel Learning for Motor Imagery BCI Illiteracy

A brain-computer interface (BCI) translates a user’s thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of BCI illiterate subjects are low classification rates and poor repeatability. To address the problem of MI-BCI illiteracy, we propose a distribution adaptation method based on multi-kernel learning to make the distribution of features between the source domain and target domain become even closer to each other, while the divisibility of categories is maximized. Inspired by the kernel trick, we adopted a multiple-kernel-based extreme learning machine to train the labeled source-domain data to find a new high-dimensional subspace that maximizes data divisibility, and then use multiple-kernel-based maximum mean discrepancy to conduct distribution adaptation to eliminate the difference in feature distribution between domains in the new subspace. In light of the high dimension of features of MI-BCI illiteracy, random forest, which can effectively handle high-dimensional features without additional cross-validation, was employed as a classifier. The proposed method was validated on an open dataset. The experimental results show that that the method we proposed suits MI-BCI illiteracy and can reduce the inter-domain differences, resulting in a reduction in the performance degradation of both cross-subjects and cross-sessions

Effect of distance penalty on average PSNR (dB)(Set 5).

<p>(A) upscaling factor ×2; (B) upscaling factor ×3; (C) upscaling factor ×4.</p

Comparisons with various image super-resolution methods on “241004” from B100 with upscaling factor ×3 (<i>σ</i> = 10, PSNR in dB).

<p>(A) Ground truth HR; (B) NE [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182165#pone.0182165.ref022" target="_blank">22</a>]; (C) SCSR [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182165#pone.0182165.ref025" target="_blank">25</a>]; (D) Zedye [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182165#pone.0182165.ref026" target="_blank">26</a>]; (E) A+ [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182165#pone.0182165.ref031" target="_blank">31</a>]; (F) SRCNN [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182165#pone.0182165.ref020" target="_blank">20</a>]; (G) CSC [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182165#pone.0182165.ref032" target="_blank">32</a>]; (H) ours.</p