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  1. Article ; Online: BO-DRNet

    Dawei Wang / Jianhua Wan / Shanwei Liu / Yanlong Chen / Muhammad Yasir / Mingming Xu / Peng Ren

    Remote Sensing, Vol 14, Iss 264, p

    An Improved Deep Learning Model for Oil Spill Detection by Polarimetric Features from SAR Images

    2022  Volume 264

    Abstract: ... learning model named BO-DRNet. The model can obtain a more sufficiently and fuller feature by ResNet-18 ... as the backbone in encoder of DeepLabv3+, and Bayesian Optimization (BO) was used to optimize the model’s ... with other deep learning models, BO-DRNet performs best with a mean accuracy of 74.69% and a mean dice of 0.8551 ...

    Abstract Oil spill pollution at sea causes significant damage to marine ecosystems. Quad-polarimetric Synthetic Aperture Radar (SAR) has become an essential technology since it can provide polarization features for marine oil spill detection. Using deep learning models based on polarimetric features, oil spill detection can be achieved. However, there is insufficient feature extraction due to model depth, small reception field lend due to loss of target information, and fixed hyperparameter for models. The effect of oil spill detection is still incomplete or misclassified. To solve the above problems, we propose an improved deep learning model named BO-DRNet. The model can obtain a more sufficiently and fuller feature by ResNet-18 as the backbone in encoder of DeepLabv3+, and Bayesian Optimization (BO) was used to optimize the model’s hyperparameters. Experiments were conducted based on ten prominent polarimetric features were extracted from three quad-polarimetric SAR images obtained by RADARSAT-2. Experimental results show that compared with other deep learning models, BO-DRNet performs best with a mean accuracy of 74.69% and a mean dice of 0.8551. This paper provides a valuable tool to manage upcoming disasters effectively.
    Keywords deep learning model ; oil spill detection ; polarization feature ; SAR images ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A monthly temperature prediction based on the CEEMDAN-BO-BiLSTM coupled model.

    Zhang, Xianqi / Ren, He / Liu, Jiawen / Zhang, Yuehan / Cheng, Wanhui

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 808

    Abstract: ... of nonlinear and nonsmooth signals, BO algorithm in optimizing the objective function within a limited number ... the future data, a monthly average temperature prediction model based on CEEMDAN-BO-BiLSTM is established ... A CEEMDAN-BO-BiLSTM-based monthly average temperature prediction model is developed and applied ...

    Abstract Temperature as an important indicator of climate change, accurate temperature prediction has important guidance and application value for agricultural production, energy management and disaster warning. Based on the advantages of CEEMDAN model in effectively extracting the time-frequency characteristics of nonlinear and nonsmooth signals, BO algorithm in optimizing the objective function within a limited number of iterations, and BiLSTM model in revealing the connection between the current data, the previous data and the future data, a monthly average temperature prediction model based on CEEMDAN-BO-BiLSTM is established. A CEEMDAN-BO-BiLSTM-based monthly average temperature prediction model is developed and applied to the prediction of monthly average temperature in Jinan City, Shandong Province. The results show that the constructed monthly mean temperature prediction model based on CEEMDAN-BO-BiLSTM is feasible; the constructed CEEMDAN-BO-BiLSTM model has an average absolute error of 1.17, a root mean square error of 1.43, an average absolute percentage error of 0.31%, which is better than CEEMDAN-BiLSTM, EMD-BiLSTM, and BiLSTM models in terms of prediction accuracy and shows better adaptability; the constructed CEEMDAN-BO-BiLSTM model illustrates that the model is not over-modeled and adds complexity using Friedman's test and performance comparisons between model run speeds. The model provides insights for effective forecasting of monthly mean temperatures.
    Language English
    Publishing date 2024-01-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-51524-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis.

    Ahmed, Asif / Song, Wei / Zhang, Yumeng / Haque, M Aminul / Liu, Xian

    Materials (Basel, Switzerland)

    2023  Volume 16, Issue 12

    Abstract: ... that both HML models can successfully predict the SCM strength values with high accuracy, and the Bo-XGB model ...

    Abstract Self-compacting mortar (SCM) has superior workability and long-term durable performance compared to traditional mortar. The strength of SCM, including both its compressive and flexural strengths, is a crucial property that is determined by appropriate curing conditions and mix design parameters. In the context of materials science, predicting the strength of SCM is challenging because of multiple influencing factors. This study employed machine learning techniques to establish SCM strength prediction models. Based on ten different input parameters, the strength of SCM specimens were predicted using two different types of hybrid machine learning (HML) models, namely Extreme Gradient Boosting (XGBoost) and the Random Forest (RF) algorithm. HML models were trained and tested by experimental data from 320 test specimens. In addition, the Bayesian optimization method was utilized to fine tune the hyperparameters of the employed algorithms, and cross-validation was employed to partition the database into multiple folds for a more thorough exploration of the hyperparameter space while providing a more accurate assessment of the model's predictive power. The results show that both HML models can successfully predict the SCM strength values with high accuracy, and the Bo-XGB model demonstrated higher accuracy (R
    Language English
    Publishing date 2023-06-13
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2487261-1
    ISSN 1996-1944
    ISSN 1996-1944
    DOI 10.3390/ma16124366
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: BN/BO-Ullazines and Bis-BO-Ullazines: Effect of BO Doping on Aromaticity and Optoelectronic Properties.

    Guo, Yongkang / Zhang, Lei / Li, Chenglong / Jin, Mengjia / Zhang, Yanli / Ye, Jincheng / Chen, Yu / Wu, Xiaoming / Liu, Xuguang

    The Journal of organic chemistry

    2021  Volume 86, Issue 18, Page(s) 12507–12516

    Abstract: We have achieved substitutional doping of ullazine with either two BO units or with one BO unit and ... borylative cyclization as the key steps. Ullazine cores of both BN/BO-ullazines ( ...

    Abstract We have achieved substitutional doping of ullazine with either two BO units or with one BO unit and one BN unit. The synthesis of these B-doped ullazines is straightforward, using demethylation and borylative cyclization as the key steps. Ullazine cores of both BN/BO-ullazines (
    Language English
    Publishing date 2021-08-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 123490-0
    ISSN 1520-6904 ; 0022-3263
    ISSN (online) 1520-6904
    ISSN 0022-3263
    DOI 10.1021/acs.joc.1c00777
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: A monthly temperature prediction based on the CEEMDAN–BO–BiLSTM coupled model

    Xianqi Zhang / He Ren / Jiawen Liu / Yuehan Zhang / Wanhui Cheng

    Scientific Reports, Vol 14, Iss 1, Pp 1-

    2024  Volume 13

    Abstract: ... characteristics of nonlinear and nonsmooth signals, BO algorithm in optimizing the objective function ... the previous data and the future data, a monthly average temperature prediction model based on CEEMDAN–BO ... BiLSTM is established. A CEEMDAN–BO–BiLSTM-based monthly average temperature prediction model is ...

    Abstract Abstract Temperature as an important indicator of climate change, accurate temperature prediction has important guidance and application value for agricultural production, energy management and disaster warning. Based on the advantages of CEEMDAN model in effectively extracting the time–frequency characteristics of nonlinear and nonsmooth signals, BO algorithm in optimizing the objective function within a limited number of iterations, and BiLSTM model in revealing the connection between the current data, the previous data and the future data, a monthly average temperature prediction model based on CEEMDAN–BO–BiLSTM is established. A CEEMDAN–BO–BiLSTM-based monthly average temperature prediction model is developed and applied to the prediction of monthly average temperature in Jinan City, Shandong Province. The results show that the constructed monthly mean temperature prediction model based on CEEMDAN–BO–BiLSTM is feasible; the constructed CEEMDAN–BO–BiLSTM model has an average absolute error of 1.17, a root mean square error of 1.43, an average absolute percentage error of 0.31%, which is better than CEEMDAN–BiLSTM, EMD–BiLSTM, and BiLSTM models in terms of prediction accuracy and shows better adaptability; the constructed CEEMDAN–BO–BiLSTM model illustrates that the model is not over-modeled and adds complexity using Friedman’s test and performance comparisons between model run speeds. The model provides insights for effective forecasting of monthly mean temperatures.
    Keywords Medicine ; R ; Science ; Q
    Subject code 541
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Design of mesoporous Ni-Co hydroxides nanosheets stabilized by BO

    Chen, Chen / Liu, Meiping / Liu, Zexue / Xie, Mingjiang / Wan, Liu / Chen, Jian / Zhang, Yan / Du, Cheng / Li, Dongsheng

    Journal of colloid and interface science

    2022  Volume 614, Page(s) 66–74

    Abstract: ... devices. In this work, mesoporous two-dimensional Ni-Co hydroxide nanosheets stabilized by BO ...

    Abstract The investigation of high-efficiency electrodes is essential for the energy conversion/storage devices. In this work, mesoporous two-dimensional Ni-Co hydroxide nanosheets stabilized by BO
    Language English
    Publishing date 2022-01-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 241597-5
    ISSN 1095-7103 ; 0021-9797
    ISSN (online) 1095-7103
    ISSN 0021-9797
    DOI 10.1016/j.jcis.2022.01.028
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: BN/BO-Ullazines and Bis-BO-Ullazines: Effect of BO Doping on Aromaticity and Optoelectronic Properties

    Guo, Yongkang / Zhang, Lei / Li, Chenglong / Jin, Mengjia / Zhang, Yanli / Ye, Jincheng / Chen, Yu / Wu, Xiaoming / Liu, Xuguang

    Journal of organic chemistry. 2021 Aug. 01, v. 86, no. 18

    2021  

    Abstract: We have achieved substitutional doping of ullazine with either two BO units or with one BO unit and ... borylative cyclization as the key steps. Ullazine cores of both BN/BO-ullazines (2) and bis-BO-ullazines (3 ...

    Abstract We have achieved substitutional doping of ullazine with either two BO units or with one BO unit and one BN unit. The synthesis of these B-doped ullazines is straightforward, using demethylation and borylative cyclization as the key steps. Ullazine cores of both BN/BO-ullazines (2) and bis-BO-ullazines (3) are very close to being planar. Their electronic and photophysical properties were investigated by ultraviolet–visible, fluorescence spectroscopy, cyclic voltammetry, and density functional theory calculations.
    Keywords demethylation ; density functional theory ; fluorescence emission spectroscopy ; organic chemistry ; voltammetry
    Language English
    Dates of publication 2021-0801
    Size p. 12507-12516.
    Publishing place American Chemical Society
    Document type Article
    ZDB-ID 123490-0
    ISSN 1520-6904 ; 0022-3263
    ISSN (online) 1520-6904
    ISSN 0022-3263
    DOI 10.1021/acs.joc.1c00777
    Database NAL-Catalogue (AGRICOLA)

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  8. Article: Ultra-low thermal conductivity in B₂O₃ composited SiGe bulk with enhanced thermoelectric performance at medium temperature region

    Nong, Jian / Peng, Ying / Liu, Chengyan / Shen, Jin Bo / Liao, Qing / Chiew, Yi Ling / Oshima, Yoshifumi / Li, Fu Cong / Zhang, Zhong Wei / Miao, Lei

    Journal of materials chemistry A. 2022 Feb. 22, v. 10, no. 8

    2022  

    Abstract: ... B₂O₃ into SiGe via ball milling and spark plasma sintering technique, aiming to realize high ... thermoelectric performance in the medium temperature region. By controlling the amount of B₂O₃ added to the SiGe ... in a high ZT of 1.47 of the p-type B₂O₃/SiGe bulk composite at 873 K. Our ZT values are 116% higher ...

    Abstract Commonly, SiGe is considered as a typical thermoelectric material with a favorable performance in the high temperature region. Here, we report a new strategy through the addition of the nano-second-phase B₂O₃ into SiGe via ball milling and spark plasma sintering technique, aiming to realize high thermoelectric performance in the medium temperature region. By controlling the amount of B₂O₃ added to the SiGe matrix, the thermal conductivity is greatly reduced because of the compound effects of the nano-second-phase and beneficial microstructure. Simultaneously, the power factors are also enhanced, resulting in a high ZT of 1.47 of the p-type B₂O₃/SiGe bulk composite at 873 K. Our ZT values are 116% higher than the typical materials used in radio-isotope thermoelectric generators (RTGs), and show 104% enhancement over the p-type SiGe alloys in the present study. This study opens a new way to widen and improve the thermoelectric performance for narrow temperature region materials, and is especially highly effective for the environmentally friendly SiGe materials.
    Keywords microstructure ; radionuclides ; temperature ; thermal conductivity
    Language English
    Dates of publication 2022-0222
    Size p. 4120-4130.
    Publishing place The Royal Society of Chemistry
    Document type Article
    ZDB-ID 2702232-8
    ISSN 2050-7496 ; 2050-7488
    ISSN (online) 2050-7496
    ISSN 2050-7488
    DOI 10.1039/d1ta09198k
    Database NAL-Catalogue (AGRICOLA)

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  9. Article: Novel Red-Emitting Ba₃Y(BO₃)₃:Bi

    Maggay, Irish Valerie B / Liu, Wei-Ren

    Journal of nanoscience and nanotechnology

    2018  Volume 18, Issue 1, Page(s) 3–10

    Abstract: Ba3Y(BO3)3:Eu3+, Bi3+ were successfully prepared via a solid-state reaction. The crystallinity, photoluminescence properties, energy transfer and thermal quenching properties were studied. Subjecting Ba3Y(BO3)3:Bi3+ samples to different excitation ... ...

    Abstract Ba3Y(BO3)3:Eu3+, Bi3+ were successfully prepared via a solid-state reaction. The crystallinity, photoluminescence properties, energy transfer and thermal quenching properties were studied. Subjecting Ba3Y(BO3)3:Bi3+ samples to different excitation wavelengths (340-370 nm), obtained blue and green emission ascribed to Bi3+(II) and Bi3+(I) sites, respectively. The influence of these two sites were systematically investigated. Bi3+ efficiently transferred its absorbed energy to neighboring Eu3+ sites by enhancing its luminescence intensity. Moreover, Bi3+ greatly enhanced the excitation spectra of Eu3+ in the N-UV region by 2.26 times which indicates that Ba3Y(BO3)3:Eu3+, Bi3+ can be used as a phosphor for w-LEDs using N-UV LED chips.
    Language English
    Publishing date 2018-05-16
    Publishing country United States
    Document type Journal Article
    ISSN 1533-4880
    ISSN 1533-4880
    DOI 10.1166/jnn.2018.14554
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: BO-DRNet: An Improved Deep Learning Model for Oil Spill Detection by Polarimetric Features from SAR Images

    Wang, Dawei / Wan, Jianhua / Liu, Shanwei / Chen, Yanlong / Yasir, Muhammad / Xu, Mingming / Ren, Peng

    Remote Sensing. 2022 Jan. 07, v. 14, no. 2

    2022  

    Abstract: ... learning model named BO-DRNet. The model can obtain a more sufficiently and fuller feature by ResNet-18 ... as the backbone in encoder of DeepLabv3+, and Bayesian Optimization (BO) was used to optimize the model’s ... with other deep learning models, BO-DRNet performs best with a mean accuracy of 74.69% and a mean dice of 0.8551 ...

    Abstract Oil spill pollution at sea causes significant damage to marine ecosystems. Quad-polarimetric Synthetic Aperture Radar (SAR) has become an essential technology since it can provide polarization features for marine oil spill detection. Using deep learning models based on polarimetric features, oil spill detection can be achieved. However, there is insufficient feature extraction due to model depth, small reception field lend due to loss of target information, and fixed hyperparameter for models. The effect of oil spill detection is still incomplete or misclassified. To solve the above problems, we propose an improved deep learning model named BO-DRNet. The model can obtain a more sufficiently and fuller feature by ResNet-18 as the backbone in encoder of DeepLabv3+, and Bayesian Optimization (BO) was used to optimize the model’s hyperparameters. Experiments were conducted based on ten prominent polarimetric features were extracted from three quad-polarimetric SAR images obtained by RADARSAT-2. Experimental results show that compared with other deep learning models, BO-DRNet performs best with a mean accuracy of 74.69% and a mean dice of 0.8551. This paper provides a valuable tool to manage upcoming disasters effectively.
    Keywords Bayesian theory ; models ; oil spills ; polarimetry ; pollution ; synthetic aperture radar
    Language English
    Dates of publication 2022-0107
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2513863-7
    ISSN 2072-4292
    ISSN 2072-4292
    DOI 10.3390/rs14020264
    Database NAL-Catalogue (AGRICOLA)

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