Different Crystallographic One-dimensional MnO2 Nanomaterials and Their Superior Performance in Catalytic Phenol Degradation

2013 ◽  
Vol 47 (11) ◽  
pp. 5882-5887 ◽  
Author(s):  
Edy Saputra ◽  
Syaifullah Muhammad ◽  
Hongqi Sun ◽  
H. M. Ang ◽  
M. O. Tadé ◽  
...  
Keyword(s):  
Phenol Degradation ◽  
Superior Performance ◽  
One Dimensional

scholarly journals Measurement Matrix Optimization via Mutual Coherence Minimization for Compressively Sensed Signals Reconstruction

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Ziran Wei ◽  
Jianlin Zhang ◽  
Zhiyong Xu ◽  
Yong Liu ◽  
Krzysztof Okarma
Keyword(s):  
Optimization Methods ◽  
Superior Performance ◽  
Gram Matrix ◽  
Sparse Signals ◽  
Two Dimensional ◽  
Measurement Matrix ◽  
Mutual Coherence ◽  
One Dimensional ◽  
Two Dimensional Image ◽  
Matrix Optimization

For signals reconstruction based on compressive sensing, to reconstruct signals of higher accuracy with lower compression rates, it is required that there is a smaller mutual coherence between the measurement matrix and the sparsifying matrix. Mutual coherence between the measurement matrix and sparsifying matrix can be expressed indirectly by the property of the Gram matrix. On the basis of the Gram matrix, a new optimization algorithm of acquiring a measurement matrix has been proposed in this paper. Firstly, a new mathematical model is designed and a new method of initializing measurement matrix is adopted to optimize the measurement matrix. Then, the loss function of the new algorithm model is solved by the gradient projection-based method of Gram matrix approximating an identity matrix. Finally, the optimized measurement matrix is generated by minimizing mutual coherence between measurement matrix and sparsifying matrix. Compared with the conventional measurement matrices and the traditional optimization methods, the proposed new algorithm effectively improves the performance of optimized measurement matrices in reconstructing one-dimensional sparse signals and two-dimensional image signals that are not sparse. The superior performance of the proposed method in this paper has been fully tested and verified by a large number of experiments.

scholarly journals Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7973
Author(s):  
Shengli Zhang ◽  
Jifei Pan ◽  
Zhenzhong Han ◽  
Linqing Guo
Keyword(s):  
Recognition Rate ◽  
Superior Performance ◽  
Signal Recognition ◽  
Noisy Environments ◽  
Soft Thresholding ◽  
One Dimensional ◽  
Signal Features ◽  
Different Types ◽  
Noisy Signals ◽  
Thresholding Function

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.

scholarly journals Assembly of CaIn2S4 on Defect-Rich BiOCl for Acceleration of Interfacial Charge Separation and Photocatalytic Phenol Degradation via S-Scheme Electron Transfer Mechanism

Catalysts ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1130
Author(s):  
Zhuangzhuang Zhang ◽  
Yuanyuan Zhang ◽  
Xuanxuan Han ◽  
Li Guo ◽  
Danjun Wang ◽  
...  
Keyword(s):  
Charge Separation ◽  
Environmental Remediation ◽  
Oxygen Vacancies ◽  
Phenol Degradation ◽  
Superior Performance ◽  
The Novel ◽  
Electron Transfer Mechanism ◽  
Synthetic Strategy ◽  
Interfacial Charge ◽  
High Temperature Calcination

The novel 2D/2D S-scheme heterostructure of BiOCl nanosheets coupled with CaIn2S4 nanosheets (CaIn2S4/BiOCl-SOVs), which contains surface oxygen vacancies (SOVs), has been successfully prepared by high-temperature calcination combined with a solvothermal synthetic strategy. Under visible-light irradiation, the apparent rate constant (Kapp/mim−1) for phenol degradation on the 1 wt% CaIn2S4/BiOCl-SOVs photocatalyst is about 32.8 times higher than that of pure BiOCl. The superior performance was attributed to the synergistic effect between the SOVs, CaIn2S4, and BiOCl, which can effectively narrow the bandgap and accelerate the interfacial charge separation of CaIn2S4/BiOCl-SOVs heterojunctions. Subsequently, it significantly promotes the generation of superoxide radicals (O2−), hydroxyl radicals, and h+, which participate in the photodegradation process of phenol. The catalyst still maintained a relatively high activity after repeated tests as a demonstration of its photostability. This work successfully proposed an efficient method to design a new 2D/2D S-scheme heterostructure with SOVs as possible photocatalysts in the field of environmental remediation.

scholarly journals Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6350
Author(s):  
Bin Wu ◽  
Shibo Yuan ◽  
Peng Li ◽  
Zehuan Jing ◽  
Shao Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Radar Signal ◽  
Signal Recognition ◽  
Electromagnetic Environment ◽  
One Dimensional ◽  
The Given ◽  
And Storage

As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.

Polyoxometalate-based layered nano-tubular arrays: facile fabrication and superior performance for catalysis

RSC Advances ◽  
2015 ◽  
Vol 5 (31) ◽  
pp. 24550-24557 ◽  
Author(s):  
Hongpeng Zhen ◽  
Xiaolin Li ◽  
Lijuan Zhang ◽  
Huan Lei ◽  
Chao Yu ◽  
...  
Keyword(s):  
Superior Performance ◽  
One Dimensional ◽  
Keggin Type ◽  
Facile Fabrication ◽  
Tubular Arrays

Keggin-type polyoxoanion PW12O403−-containing one-dimensional nano-tubular arrays fabricated within porous templates show a superior performance just through simple filtrating processes.

A sensitive gel-based global O-glycomics approach reveals high levels of mannosyl glycans in the high mass region of the mouse brain proteome

2012 ◽  
Vol 393 (8) ◽  
pp. 709-717 ◽  
Author(s):  
Isabelle Breloy ◽  
Sandra Pacharra ◽  
Christina Aust ◽  
Franz-Georg Hanisch
Keyword(s):  
Mouse Brain ◽  
Gel Electrophoresis ◽  
Relative Proportion ◽  
Mass Region ◽  
Superior Performance ◽  
High Mass ◽  
Robust Performance ◽  
One Dimensional ◽  
Complex Protein ◽  
Brain Proteome

Abstract We developed a gel-based global O-glycomics method applicable for highly complex protein mixtures entrapped in discontinuous gradient gel layers. The protocol is based on in-gel proteolysis with pronase followed by (glyco)peptide elution and off-gel reductive β-elimination. The protocol offers robust performance with sensitivity in the low picomolar range, is compatible with gel-based proteomics, and shows superior performance in global applications in comparison with workflows eliminating glycans in-gel or from electroblotted glycoproteins. By applying this method, we analyzed the O-glycome of human myoblasts and of the mouse brain O-glycoproteome. After semipreparative separation of mouse brain proteins by one-dimensional SDS gel electrophoresis, the O-glycans from proteins in different mass ranges were characterized with a focus on O-mannose-based glycans. The relative proportion of the latter, which generally represent a rare modification, increases to comparatively high levels in the mouse brain proteome in dependence of increasing protein masses.

scholarly journals A novel type of one-dimensional organic selenium-containing fiber with superior performance for lithium–selenium and sodium–selenium batteries

RSC Advances ◽  
2014 ◽  
Vol 4 (106) ◽  
pp. 61673-61678 ◽  
Author(s):  
Hongqiang Wang ◽  
Sha Li ◽  
Zhixin Chen ◽  
Hua Kun Liu ◽  
Zaiping Guo
Keyword(s):  
Superior Performance ◽  
One Dimensional ◽  
Organic Selenium

Facile synthesis of nanostructured TiNb2O7 anode materials with superior performance for high-rate lithium ion batteries

2015 ◽  
Vol 51 (97) ◽  
pp. 17293-17296 ◽  
Author(s):  
Shuaifeng Lou ◽  
Yulin Ma ◽  
Xinqun Cheng ◽  
Jinlong Gao ◽  
Yunzhi Gao ◽  
...  
Keyword(s):  
Anode Materials ◽  
Rate Capability ◽  
Lithium Ion ◽  
Reversible Capacity ◽  
High Rate ◽  
Superior Performance ◽  
Facile Synthesis ◽  
One Dimensional ◽  
Excellent Electrochemical Performance ◽  
The One

The one-dimensional nanostructured TiNb2O7 exhibited excellent electrochemical performance with superior reversible capacity, rate capability and cyclic stability.

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