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2nd Asia-Pacific Conference on Communications Technology and Computer Science, ACCTCS 2022

作者： Wang, Feng Chen, xujiang Wang, Xiangxu xu, shuwen Yan, Diantao China Electric Power Research Institute Co. Ltd Beijing100192 China

ISBN: (纸本)9781665400343

In view of the increasing proportion of renewable energy access in distribution network, the traditional programming method was unable to meet the needs of power grid development. A hierarchical and zoned energy-saving programming method for distribution network with high proportion of renewable energy was proposed. Firstly, the capacity and layout of high proportion renewable energy were optimized to realize the initial site selection and capacity determination of renewable energy. Secondly, taking the renewable energy planning results as the initial value, a bi-level iterative programming model was constructed to realize the optimization decision making model with cost and energy-saving benefits as the goal. Finally, the harmony search algorithm was used to solve the proposed optimization model. The real power grid data is used for analysis. The simulation results show the effectiveness and feasibility of the proposed model and method. © 2022 IEEE.

关键词： Energy conservation

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Fabrication and characterization of Chinese yam polysaccharides PLGA nanoparticles stabilized Pickering emulsion as an efficient adjuvant

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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 2022年第PartA期209卷 513-524页

作者： Zhang, Yue Jiao, Lina Wu, Zhiyong Gu, Pengfei Feng, Zian xu, shuwen Liu, Zhenguang Yang, Yang Wang, Deyun Inst Tradit Chinese Vet Med Coll Vet Med Nanjing Peoples R China Nanjing Agr Univ Coll Vet Med MOE Joint Int Res Lab Anim Hlth & Food Safety Nanjing 210095 Peoples R China Nanjing Tradit Chinese Vet Med Res Ctr Nanjing 210095 Peoples R China Nanjing Agr Univ Coll Vet Med Nanjing 210095 Peoples R China

The Chinese yam polysaccharides PLGA nanoparticles were applied as stabilizers in this study to prepare O/W Pickering emulsion. The optimized preparation conditions were PLGA concentration of 5 mg/mL, ultrasonic power of 50 %, and ultrasonic time of 2 min. The CYP-PPAS emulsion exhibits a raspberry-like morphology with a large number of nanoparticles surrounding the oil droplets. The CYP-PPAS emulsion exhibited outstanding stability at 4 ? and 37 334602623_20220426091804361 / urn:si:7508211 for 28 days with high antigen loading efficiency and provided a controlled and sustained release of Chinese yam polysaccharides and OVA antigen in vitro. CYP-PPAS/OVA elicited robust antigen-specific immune response and induced a mixed Th1/Th2 immune response after immunization. Furthermore, CYP-PPAS/OVA caused a high CD4+/CD8+ ratio leading in increased activation of splenic T lymphocytes subpopulations. Moreover, CYP-PPAS is a safe vaccination adjuvant with high safety profile in vivo. Thus, the novel designed Pickering emulsion CYP-PPAS was a safe and effective adjuvant for inducing the strong and long-term immune response.

关键词： Chinese yam polysaccharides Pickering emulsion Adjuvant

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Marine Radar Small Target Classification Based on Block-Whitened TimeFrequency Spectrogram and Pre-Trained CNN

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： xu, shuwen Ru, Hongtao Li, Dongchen Shui, Penglang xue, Jian Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Xian 710071 Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada China State Shipbldg Corp Syst Engn Res Inst Beijing 100036 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Xian Univ Posts & Telecommun Sch Artificial Intelligence Xian 710121 Peoples R China

This article presents a classification method to classify different marine floating small targets, which can realize effective classification of different targets in strong clutter background. The design of proposed classification method is primarily based on block-whitened time-frequency spectrogram and pre-trained convolution neural network (CNN). Block-whitening clutter suppression is used to process target echoes. By converting a strong clutter background to an approximately noisy background, the effect of strong clutter on classification is reduced. Then, the time-frequency spectrogram of targets is extracted from the block-whitened target echoes, which converts a signal in time domain into a time-frequency spectrogram with more information. In addition, the block-whitened time-frequency spectrograms are input to a pre-trained CNN for feature extraction and classification training. By exploiting pre-training procedure, the proposed method can effectively classify different marine floating small targets and solve the problem of limited target samples in practical applications. Finally, a dataset of three kinds of measured maritime radar targets is constructed to verify the effectiveness of proposed method. Experimental results show that compared with competitors, the pre-trained CNN with block-whitened time-frequency spectrograms can achieve higher performance on the measured dataset.

关键词： Block whitening floating small target pretrained convolution neural network (CNN) target classification time-frequency spectrograms

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基于卷积与自注意力的红外与可见光图像融合

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系统工程与电子技术 2024年第8期46卷 2641-2649页

作者：陈晓萱徐书文胡绍海马晓乐北京交通大学计算机与信息技术学院北京100044 中国电子科技集团公司电视电声研究所北京100015

由于卷积运算过于关注图像的局部特征,在对源图像进行融合时容易造成融合图像的全局语义信息丢失。为了解决该问题,提出一种基于卷积与自注意力的红外与可见光图像融合模型。该模型在使用卷积模块提取图像局部特征的同时,还使用自注意... 详细信息

由于卷积运算过于关注图像的局部特征,在对源图像进行融合时容易造成融合图像的全局语义信息丢失。为了解决该问题,提出一种基于卷积与自注意力的红外与可见光图像融合模型。该模型在使用卷积模块提取图像局部特征的同时,还使用自注意力来提取图像全局特征。此外,由于简单运算无法满足不同层次特征的融合,提出使用嵌入式块残差融合模块来实现多层次特征融合。实验结果表明,相比无监督深度融合算法,所提的方法在主观评价与6项客观指标上的结果具有一定优势。其中,互信息、标准差和视觉保真度分别提升了61.33%、9.96%和19.46%。

关键词：图像融合全局特征自注意力机制自编码器深度学习

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Cationic Nanoparticle-Stabilized Vaccine Delivery System for the H9N2 Vaccine to Promote Immune Response in Chickens

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MOLECULAR PHARMACEUTICS 2023年第3期20卷 1613-1623页

作者： Zhang, Yue Zhu, Tianyu xu, shuwen Gu, Pengfei Cai, Gaofeng Peng, Song Liu, Zhenguang Yang, Yang Hu, Yuanliang Liu, Jiaguo Wang, Deyun Inst Tradit Chinese Vet Med Coll Vet Med Nanjing 210095 Jiangsu Peoples R China Nanjing Agr Univ Coll Vet Med MOE Joint Int Res Lab Anim Hlth & Food Safety Nanjing 210095 Peoples R China

Chinese yam polysaccharides (CYPs) have received wide attention for their immunomodulatory activity. Our previous studies had discovered that the Chinese yam polysaccharide PLGA-stabilized Pickering emulsion (CYP-PPAS) can serve as an efficient adjuvant to trigger powerful humoral and cellular immunity. Recently, positively charged nano-adjuvants are easily taken up by antigen-presenting cells, potentially resulting in lysosomal escape, the promotion of antigen cross-presentation, and the induction of CD8 T-cell response. However, reports on the practical application of cationic Pickering emulsions as adjuvants are very limited. Considering the economic damage and public-health risks caused by the H9N2 influenza virus, it is urgent to develop an effective adjuvant for boosting humoral and cellular immunity against influenza virus infection. Here, we applied polyethyleneimine-modified Chinese yam polysaccharide PLGA nanoparticles as particle stabilizers and squalene as the oil core to fabricate a positively charged nanoparticle-stabilized Pickering emulsion adjuvant system (PEI-CYP-PPAS). The cationic Pickering emulsion of PEI-CYP-PPAS was utilized as an adjuvant for the H9N2 Avian influenza vaccine, and the adjuvant activity was compared with the Pickering emulsion of CYP-PPAS and the commercial adjuvant (aluminum adjuvant). The PEI-CYP-PPAS, with a size of about 1164.66 nm and a zeta potential of 33.23 mV, could increase the H9N2 antigen loading efficiency by 83.99%. After vaccination with Pickering emulsions based on H9N2 vaccines, PEI-CYP-PPAS generated higher HI titers and stronger IgG antibodies than CYP-PPAS and Alum and increased the immune organ index of the spleen and bursa of Fabricius without immune organ injury. Moreover, treatment with PEI-CYP-PPAS/H9N2 induced CD4+ and CD8+ T-cell activation, a high lymphocyte proliferation index, and increased cytokine expression of IL-4, IL-6, and IFN-gamma. Thus, compared with the CYP-PPAS and aluminum adjuvant, the

关键词： Chinese yam polysaccharides avian influenza viruses polyethyleneimine poly (lactic-co-glycolic acid) Pickering emulsion adjuvant

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Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery

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REMOTE SENSING 2023年第8期15卷 2196-2196页

作者： xu, shuwen Yu, Tan xu, Jinmeng Pan, Xishan Shao, Weizeng Zuo, Juncheng Yu, Yang Donghai Lab Zhoushan 316021 Peoples R China Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China Key Lab Marine Ecol Monitoring & Restorat Technol Shanghai 201206 Peoples R China Tidal Flat Res Ctr State Ocean Adm Nanjing 210036 Peoples R China Minist Transport Key Lab Port Waterway & Sedimentat Engn Nanjing 210029 Peoples R China Minist Nat Resources Natl Satellite Ocean Applicat Serv Beijing 100081 Peoples R China Qingdao Univ Sci & Technol Dept Marine Sci & Biol Qingdao 266042 Peoples R China

This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, Recall, and F1-score, showed that our extraction method can be applied to the images of most satellites and different environments. We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April. The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide, and uncertainty for the predicted growth curve was estimated. The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15, respectively, and the accumulative cover area was expected to be approximately 1190.90-1191.21 km(2).

关键词： Yellow Sea green tide NDVI multi-source RS source tracing growth curve prediction

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Rao Test for Radar Target Detection in Sub-Gaussian Symmetric Alpha-Stable Sea Clutter

Rao Test for Radar Target Detection in Sub-Gaussian Symmetri...

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2021 CIE International Conference on Radar, Radar 2021

作者： Liu, xu Li, Yachao xu, shuwen Du, Lan Academy of Advanced Interdisciplinary Research Xidian University Xi'an China Processing Xidian University National Laboratory of Radar Signal Xi'an China

ISBN: (纸本)9781665498142

This paper presents an adaptive detector based on maximum a posteriori (MAP) Rao test specially for the sub-Gaussian symmetric alpha-stable (SGSαS) sea clutter background. Since SGSαS distribution does not have a closed-form probability density function (PDF) expression, there are few researches on the problem of target detection in SGSαS clutter background. In this paper, firstly the closed-form PDF expression of the SGSαS distribution is given in terms of the Fox's H-function. Then the MAP Rao-SGSαS detector in alpha-stable clutter is designed based on two-step MAP Rao test. Experimental results based on the simulated data verify the effectiveness of the proposed detector. © 2021 IEEE.

关键词： Alpha-stable distribution Coherent detection Rao test Sub-Gaussian symmetric alpha-stable (SGSαS) distribution

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HRRP Clutter Rejection Via One-Class Classifier With Hausdorff Distance

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2020年第4期56卷 2517-2526页

作者： Du, Lan Liu, xu Li, Bo xu, shuwen Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Lots of detectors for high resolution range profile (HRRP) data are mainly based on the intensities of target echoes. When the intensities of outliers are as large as those of targets of interest, some false alarms will emerge during the detection stage. In this article, we propose a rejection algorithm for HRRP data between the detection stage and recognition stage to eliminate the false alarms occurring during the detection stage. In this method, the intensities and positions of the dominant scatterers are extracted as a joint feature. Then, the feature is used to develop the K-center one-class classifier based on Hausdorff distance. Experimental results on the measured HRRP data indicate that the proposed rejection algorithm can eliminate the false alarms remarkably well, and keep most of the target data as well. Meanwhile, the proposed method has better performance under different values of signal-to-noise ratio and different values of parameters than some other rejection methods.

关键词： Feature extraction Clutter Detectors Training Radar Flowcharts Target recognition Feature extraction Hausdorff distance (HD) high resolution range profile (HRRP) one-class classifier rejection

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Small Target Detection Method in Sea Clutter Based on Interframe Multi-feature Iteration 6

Small Target Detection Method in Sea Clutter Based on Interf...

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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： xu, shuwen Ma, Yitu Bai, Xiaohui Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9780738133737

For marine radar, since sea clutter is complex and dynamic, and small targets are difficult to model, the detection of floating small targets in sea clutter is always the focus and difficulty of radar target detection. The radar cross section of the floating small targets is small and the velocity is slow, so traditional detection methods have some bottlenecks, which belong to the problem of "over-clutter detection". The multi-feature-based detection method is an effective way to solve the detection of small targets on the sea surface, but traditional feature-based detection generally requires long accumulated time. Traditional scanning radar needs to take into account the surveillance space, which makes the number of accumulated pulses at each beam position restricted. It limits the application of feature detection methods to scanning radars. In this paper, aiming at the problem that the radar cannot stare at an angle for a long time in scanning mode, three features of relative average amplitude, relative Doppler peak height, and relative Doppler vector entropy are firstly extract. After that, the idea of multi-feature iteration between frames is used to update the feature values in the feature space, and then the fast convex hull algorithm is used to construct the decision region for detection. The measured data verifies that the proposed method can be well applied to scanning radar and obtains better detection performance than original methods. © 2021 IEEE.

关键词： Radar cross section

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A Forest Fire Susceptibility Modeling Approach Based on Light Gradient Boosting Machine Algorithm

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REMOTE SENSING 2022年第17期14卷 4362-4362页

作者： Sun, Yanyan Zhang, Fuquan Lin, Haifeng xu, shuwen Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

A forest fire susceptibility map generated with the fire susceptibility model is the basis of fire prevention resource allocation. A more reliable susceptibility map helps improve the effectiveness of resource allocation. Thus, further improving the prediction accuracy is always the goal of fire susceptibility modeling. This paper developed a forest fire susceptibility model based on an ensemble learning method, namely light gradient boosting machine (LightGBM), to produce an accurate fire susceptibility map. In the modeling, a subtropical national forest park in the Jiangsu province of China was used as the case study area. We collected and selected eight variables from the fire occurrence driving factors for modeling based on correlation analysis. These variables are from topographic factors, climatic factors, human activity factors, and vegetation factors. For comparative analysis, another two popular modeling methods, namely logistic regression (LR) and random forest (RF) were also applied to construct the fire susceptibility models. The results show that temperature was the main driving factor of fire in the area. In the produced fire susceptibility map, the extremely high and high susceptibility areas that were classified by LR, RF, and LightGBM were 5.82%, 18.61%, and 19%, respectively. The F1-score of the LightGBM model is higher than the LR and RF models. The accuracy of the model of LightGBM, RF, and LR is 88.8%, 84.8%, and 82.6%, respectively. The area under the curve (AUC) of them is 0.935, 0.918, and 0.868, respectively. The introduced ensemble learning method shows better ability on performance evaluation metrics.

关键词： machine learning fire factors prediction fire susceptibility map

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