Graph can achieve good performance to extract the low-dimensional features of hyperspectral image (HSI). However, the present graph-based methods just consider the individual information of each ...sample in a certain characteristic, which is very difficult to represent the intrinsic properties of HSI for the complex imaging condition. To better represent the low-dimensional features of HSI, we propose a multistructure unified discriminative embedding (MUDE) method, which considers the neighborhood, tangential, and statistical properties of each sample in HSI to achieve the complementarity of different characteristics. In MUDE, we design the intraclass and interclass neighborhood structure graphs with the local reconstruction structure of each sample; meanwhile, we also utilize the adaptive tangential affine combination structure to construct the intraclass and interclass tangential structure graphs. To further enhance the discriminating performance between different classes, we consider the influence of the statistical distribution difference for each sample to develop an interclass Gaussian weighted scatter model. Then, an embedding objective function is constructed to enhance the intraclass compactness and the interclass separability and obtain more discriminative features for HSI classification. Experiments on three real HSI datasets show that the proposed method can make full use of the structure information of each sample in different characteristics to achieve the complementarity of different features and improve the classification performance of HSI compared with the state-of-the-art methods.
The discovery and identification of novel active sites are paramount for deepening the understanding of the catalytic mechanism and driving the development of remarkable electrocatalysts. Here, we ...reveal that the genuine active sites for the hydrogen evolution reaction (HER) in LaRuSi are Si sites, not the usually assumed Ru sites. Ru in LaRuSi has a peculiar negative valence state, which leads to strong hydrogen binding to Ru sites. Surprisingly, the Si sites have a Gibbs free energy of hydrogen adsorption that is near zero (0.063 eV). The moderate adsorption of hydrogen on Si sites during the HER process is also validated by in situ Raman analysis. Based on it, LaRuSi exhibits an overpotential of 72 mV at 10 mA cm−2 in alkaline media, which is close to the benchmark of Pt/C. This work sheds light on the recognition of real active sites and the exploration of innovative silicide HER electrocatalysts.
Unlike other Ru‐containing compounds whose active sites are Ru sites, the Si sites in LaRuSi function as real active sites. The unusual negative valence Ru in this compound has excessively tight adsorption for hydrogen, according to both theoretical calculations and in situ Raman observations, but the Si sites have excellent hydrogen adsorption properties.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The separate modulation of the adsorption of *O and *OOH is challenging in oxygen evolution reaction (OER), which results in a large overpotential and slow kinetics. To balance the adsorption of the ...two active species, here, a way to regulate the local spin state and band structure simultaneously in Ni3S2 nanosheets is reported. The adequate doping of W heteroatoms causes the electron depletion from the Ni active site, which modulates the spin state of eg electrons, weakening the adsorption of *O. Additionally, the introduction of S vacancies contributes to the upshift of the d band center, which strengthens the adsorption of *OOH. In this manner, the adsorption of Ni3S2 for the active intermediates is optimized, resulting in a considerably improved overpotential of 246 mV at 100 mA cm−2 and a Tafel slope of 66 mV dec−1. This work provides insights into the exploration of OER catalysts through synergistic modulation of the spin state and the band structure.
Ni3S2 is co‐doped by W heteroatoms and S vacancies to regulate the local spin state and band structure simultaneously for advanced activity of oxygen evolution reaction.
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Optimizing the hydrogen adsorption Gibbs free energy (ΔGH) of active sites is essential to improve the overpotential of the electrocatalytic hydrogen evolution reaction (HER). We doped graphene‐like ...Co0.85Se with sulfur and found that the active sites are reversed (from cationic Co sites to anionic S sites), which contributed to an enhancement in electrocatalytic HER performance. The optimal S‐doped Co0.85Se composite has an overpotential of 108 mV (at 10 mA cm−2) and a Tafel slope of 59 mV dec−1, which exceeds other reported Co0.85Se‐based electrocatalysts. The doped S sites have much higher activity than the Co sites, with a hydrogen adsorption Gibbs free energy (ΔGH) close to zero (0.067 eV), which reduces the reaction barrier for hydrogen production. This work provides inspiration for optimizing the intrinsic HER activity of other related transition metal chalcogenides.
Graphene‐like Co0.85Se was doped with sulfur, bringing about a reversal in active sites for the electrocatalytic hydrogen evolution reaction (HER) from cationic cobalt sites to anionic sulfur sites. A consequent change in the hydrogen adsorption Gibbs free energy (ΔGH) of the active sites improved the overpotential of the HER.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Significant efforts have been dedicated to boosting the electrocatalytic activity of Co9S8 for the oxygen evolution reaction (OER); however, with limited improvement in its intrinsic activity, which ...relies on careful band engineering. Fe possesses one less 3d electron and lower electronegativity than Co, suggesting a higher d‐band center when forming polyhedron with S anions. Here, to improve the intrinsic activity by elevating the d‐band center, the six‐coordinated octahedrons in Co9S8 are redesigned utilizing an Fe‐incorporated topochemical deintercalation method. Through substituting partial Co octahedrons by Fe octahedrons with higher d‐band, the overall d‐band center is regulated to achieve optimized adsorption and thus superior OER activity. With a reduction of 95 mV on the overpotential (at 10 mA cm−2), this work sheds lights on the design of OER catalysts through polyhedron engineering using topochemical deintercalation.
A series of Fe‐doped ultrasmall Co9S8 nanoparticles with a mean size of ≈4.5 nm are prepared through solid‐state synthesis and successive topochemical deintercalation to enhance oxygen evolution reaction performance.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Alloying noble metals with non‐noble metals is a promising method to fabricate catalysts, with the advantages of reduced noble metal usage and excellent activity. In this work, electron‐abundant ...Ir/Rh sites, as highly active centers for the hydrogen evolution reaction (HER), are realized by fabricating Ir1−xRhxSb alloys through the arc‐melting method. The electron transfer from Sb to Ir/Rh makes the latter negatively charged, leading to considerably optimized adsorption for active H species during HER. As a result, the Ir1−xRhxSb alloy exhibits outstanding activity for HER, with an optimized overpotential of 22 mV at 10 mA cm–2 and a Tafel slope of 47.6 mV dec–1. This work provides insights into highly active alloys and sheds light on the utilization of electron‐abundant metal atoms.
A ternary Ir1−xRhxSb intermetallic alloy is prepared through the arc‐melting method, with negatively charged metal sites for the hydrogen evolution reaction.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Deep convolutional neural networks (CNNs) have become one of the state-of-the-art methods for image classification in various domains. For biomedical image classification where the number of training ...images is generally limited, transfer learning using CNNs is often applied. Such technique extracts generic image features from nature image datasets and these features can be directly adopted for feature extraction in smaller datasets. In this paper, we propose a novel deep neural network architecture based on transfer learning for microscopic image classification. In our proposed network, we concatenate the features extracted from three pretrained deep CNNs. The concatenated features are then used to train two fully-connected layers to perform classification. In the experiments on both the 2D-Hela and the PAP-smear datasets, our proposed network architecture produces significant performance gains comparing to the neural network structure that uses only features extracted from single CNN and several traditional classification methods.
Real-time driver distraction detection is the core to many distraction countermeasures and fundamental for constructing a driver-centered driver assistance system. While data-driven methods ...demonstrate promising detection performance, a particular challenge is how to reduce the considerable cost for collecting labeled data. This paper explored semi-supervised methods for driver distraction detection in real driving conditions to alleviate the cost of labeling training data. Laplacian support vector machine and semi-supervised extreme learning machine were evaluated using eye and head movements to classify two driver states: attentive and cognitively distracted. With the additional unlabeled data, the semi-supervised learning methods improved the detection performance (G-mean) by 0.0245, on average, over all subjects, as compared with the traditional supervised methods. As unlabeled training data can be collected from drivers' naturalistic driving records with little extra resource, semi-supervised methods, which utilize both labeled and unlabeled data, can enhance the efficiency of model development in terms of time and cost.
In this paper, we propose an improved learning algorithm named self-adaptive evolutionary extreme learning machine (SaE-ELM) for single hidden layer feedforward networks (SLFNs). In SaE-ELM, the ...network hidden node parameters are optimized by the self-adaptive differential evolution algorithm, whose trial vector generation strategies and their associated control parameters are self-adapted in a strategy pool by learning from their previous experiences in generating promising solutions, and the network output weights are calculated using the Moore–Penrose generalized inverse. SaE-ELM outperforms the evolutionary extreme learning machine (E-ELM) and the different evolutionary Levenberg–Marquardt method in general as it could self-adaptively determine the suitable control parameters and generation strategies involved in DE. Simulations have shown that SaE-ELM not only performs better than E-ELM with several manually choosing generation strategies and control parameters but also obtains better generalization performances than several related methods.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Linear phase is an important characteristic of digital filters in many signal processing applications. In this paper, two iterative reweighted minimax phase error algorithms are proposed to design ...nearly linear-phase infinite impulse response (IIR) digital filters with prescribed or simultaneously minimized magnitude errors and preset transition-band gain. In each iteration of the algorithms, a weighted minimax phase error problem with a fixed weight function is firstly solved using a modified Gauss-Newton method with a variable step length, and the weight function of the phase error is then updated using the square root of a modified envelope of the group-delay error of the filter. With the proposed methods, both very small phase error and group-delay error have been obtained while meeting the requirements on the passband and stopband magnitude errors and the transition-band gain. Design examples demonstrate the effectiveness of the proposed methods and the excellent performance of the designed filters.
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