Lattice Boltzmann Method for Fluid-Thermal Systems: Status, Hotspots, Trends and Outlook

Li, Lin; Wan, Yuehua; Lu, Jian‐Fei; Fang, Hui; Yin, Zichao; Wang, Tong; Wang, Ronghui; Fan, Xinghua; Zhao, Linjie; Tan, Dapeng

doi:10.1109/access.2020.2971546

Cited by 58 publications

(28 citation statements)

References 233 publications

(224 reference statements)

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“…Bibliometrics is defined as measuring academic and organizational performance based on several indicators such as number of publications, number of citations, and collaboration networks [28]. Bibliometric analysis is a powerful tool for quantitatively investigating scientific outputs [29]. Bibliometric analysis of scholarly publications, lead to development of different metrics gaining insight into the intellectual structure of a broad academic discipline and evaluating the impact of scientific documents [30], [31].…”

Section: Introductionmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

et al. 2020

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Time-series classification is utilized in a variety of applications leading to the development of many data mining techniques for time-series analysis. Among the broad range of time-series classification algorithms, recent studies are considering the impact of deep learning methods on time-series classification tasks. The quantity of related publications requires a bibliometric study to explore most prominent keywords, countries, sources and research clusters. The paper conducts a bibliometric analysis on related publications in time-series classification, adopted from Scopus database between 2010 and 2019. Through keywords cooccurrence analysis, a visual network structure of top keywords in time-series classification research has been produced and deep learning has been introduced as the most common topic by additional inquiry of the bibliography. The paper continues by exploring the publication trends of recent deep learning approaches for time-series classification. The annual number of publications, the productive and collaborative countries, the growth rate of sources, the most occurred keywords and the research collaborations are revealed from the bibliometric analysis within the study period. The research field has been broken down into three main categories as different frameworks of deep neural networks, different applications in remote sensing and also in signal processing for time-series classification tasks. The qualitative analysis highlights the categories of top citation rate papers by describing them in details. INDEX TERMS Time-series classification, deep learning, remote sensing, signal processing, bibliometrics, research productivity.

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Section: Introductionmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

et al. 2020

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“…In this paper, this situation can be met as shown in Equation (11) and then the iterative deconvolution is achieved using Equation (18). This is a similar procedure as iterative time reversal processing.…”

Section: Iterative Deconvolution-time Reversal Methodsmentioning

confidence: 99%

“…The result of MF is generated according to Equation (10). The result of generalized target reflectivity density function ρ(τ) is obtained according to Equation (18). The result of reflectivity density function of the target c(v) is obtained according to Equation (21).…”

Section: The Case In Free Field Environmentsmentioning

confidence: 99%

“…As shown in Figure 1, the width of main lobe of range is 0.3 m for the MF, whereas the resolution after iterative deconvolution with iteration number being 10 is approximately a delta function. The process of iterative deconvolution is implemented as iterative time reversal processing, as shown in Equation (18). With the number of iterations increasing, the ambiguity caused by the transmitted signal is more and more removed by deconvolution.…”

Section: The Case In Free Field Environmentsmentioning

confidence: 99%

“…The other way to combat noise is the design of image reconstruction, which perform Wiener filtering [15] or rational orthogonal wavelet analysis filter bank [16] to suppress noise in active radar on the basis of MIMO waveform design. The shallow water environment is a complicated media with the sea surface upward and the seafloor downward [17,18]. For active sonar in shallow water environments, since the received signal is distorted due to the time-delay and Doppler spread caused by the channel, the received signal comprises the ambiguity of both the transmitted signal and the channel.…”

Section: Introductionmentioning

confidence: 99%

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An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

Guo

Zhao

2020

Sensors

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Matched filtering is widely used in active sonar because of its simplicity and ease of implementation. However, the resolution performance generally depends on the transmitted waveform. Moreover, its detection performance is limited by the high-level sidelobes and seriously degraded in a shallow water environment due to time spread induced by multipath propagation. This paper proposed a method named iterative deconvolution-time reversal (ID-TR), on which the energy of the cross-ambiguity function is modeled, as a convolution of the energy of the auto-ambiguity function of the transmitted signal with the generalized target reflectivity density. Similarly, the generalized target reflectivity density is a convolution of the spread function of channel with the reflectivity density of target as well. The ambiguity caused by the transmitted signal and the spread function of channel are removed by Richardson-Lucy iterative deconvolution and the time reversal processing, respectively. Moreover, this is a special case of the Richardson-Lucy algorithm that the blur function is one-dimensional and time-invariant. Therefore, the iteration deconvolution is actually implemented by the iterative temporal time reversal processing. Due to the iterative time reversal method can focus more and more energy on the strongest target with the iterative number increasing and then the peak-signal power increases, the simulated result shows that the noise reduction can achieve 250 dB in the “ideal” free field environment and 100 dB in a strong multipaths waveguide environment if a 1-ms linear frequency modulation with a 4-kHz frequency bandwidth is transmitted and the number of iteration is 10. Moreover, the range resolution is approximately a delta function. The results of the experiment in a tank show that the noise level is suppressed by more than 70 dB and the reverberation level is suppressed by 3 dB in the case of a single target and the iteration number being 8.

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Promoted calcium looping H ₂ production via catalytic reforming of polycyclic aromatic hydrocarbon using a synthesized CO ₂ absorbent prepared by impregnation

Han

et al. 2021

Int J Energy Res

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Summary Promoting multicycle CO2 absorption ability and mechanical strength while enhancing heavy tar elimination performance of calcium‐based absorbents is a significant challenge for calcium looping gasification (CLG). A synthesized absorbent Fe/Ca‐Al was developed by an impregnation method consisting of two steps, during which an inert support and an iron catalytic component were integrated with calcium oxide (CaO). Fe/Ca‐Al was compared with three other candidate absorbents in terms of their basic properties and performances. Results indicated that the major components of the synthesized Fe/Ca‐Al were CaO, mayenite (Ca12Al14O33), and Ca2Fe2O5. The CO2 absorption ability of Fe/Ca‐Al gradually increased within 10 carbonation–calcination cycles and the mechanical strength was apparently promoted by the component Ca12Al14O33. Fe/Ca‐Al approached the largest conversion for the heavy tar model component 1‐methylnaphthalene (70.6%), the highest H2 yield (0.18 mol/[hr·g]), and the least coke deposition (8.18 mg/g) during reforming. The synergistic effects of Ca12Al14O33 and Ca2Fe2O5 on the performance of Fe/Ca‐Al were analyzed. The effects of the mass ratio Fe/CaO, temperature of reforming reaction, molar ratio of S/C in tar, and the absorbent particle size on the reforming performance of heavy tar were also explored. The results of this study should provide useful information and possible solutions for CLG to overcome development obstacles. Novelty Statement The present work simultaneously addresses the key challenges of CaO‐based absorbents, including not only cyclic carbonation reactivity and mechanical strength of the absorbents but also heavy tar reduction performance and the influence of different reaction conditions, which are rarely reported in the literature. A synthesized iron‐calcium absorbent is developed by a two‐step impregnation method, which is beneficial to enhance biomass heavy tar (such as PAH) reduction, cyclic CO2 capture, and mechanical strength of the absorbent for hydrogen production in calcium looping gasification.

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Lattice Boltzmann Method for Fluid-Thermal Systems: Status, Hotspots, Trends and Outlook

Cited by 58 publications

References 233 publications

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

Promoted calcium looping H ₂ production via catalytic reforming of polycyclic aromatic hydrocarbon using a synthesized CO ₂ absorbent prepared by impregnation

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Lattice Boltzmann Method for Fluid-Thermal Systems: Status, Hotspots, Trends and Outlook

Cited by 58 publications

References 233 publications

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning

An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

Promoted calcium looping H 2 production via catalytic reforming of polycyclic aromatic hydrocarbon using a synthesized CO 2 absorbent prepared by impregnation

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Promoted calcium looping H ₂ production via catalytic reforming of polycyclic aromatic hydrocarbon using a synthesized CO ₂ absorbent prepared by impregnation