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学者姓名：卢毅敏
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Abstract ：
Typhoon disasters not only trigger secondary disasters, such as rainstorms and flooding, but also bring many negative impacts on the normal operation of urban infrastructure and the safety of people's lives and property. In order to effectively prevent the risks of typhoon disaster chain, this paper proposes a joint entity and relation extraction model based on RoBERTa-Adv-GPLinker. Then, relying on the ontology theory and methodology, we establish a knowledge graph of typhoon disaster chain. The results show that the joint extraction model based on RoBERTa-Adv-GPLinker outperforms other baseline models in all assessment indexes. Moreover, the constructed knowledge graph of typhoon disaster chain includes secondary disasters and derived disaster impacts. This can largely depict the evolution process of typhoon disaster secondary derivations. On this basis, a risk assessment model of typhoon disaster chain based on Bayesian network is established. Taking Fujian Province as an example, the risk associated with the typhoon disaster chain is assessed, verifying the effectiveness of the method. This study provides a scientific basis for enhancing government emergency response capabilities and achieving sustainable regional development.
Keyword ：
Bayesian network Bayesian network disaster chain disaster chain joint extraction model joint extraction model knowledge graph knowledge graph ontology ontology typhoon disaster typhoon disaster
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| GB/T 7714 | Lu, Yimin , Qiao, Shiting , Yao, Yiran . Risk Assessment of Typhoon Disaster Chain Based on Knowledge Graph and Bayesian Network [J]. | SUSTAINABILITY , 2025 , 17 (1) . |
| MLA | Lu, Yimin 等. "Risk Assessment of Typhoon Disaster Chain Based on Knowledge Graph and Bayesian Network" . | SUSTAINABILITY 17 . 1 (2025) . |
| APA | Lu, Yimin , Qiao, Shiting , Yao, Yiran . Risk Assessment of Typhoon Disaster Chain Based on Knowledge Graph and Bayesian Network . | SUSTAINABILITY , 2025 , 17 (1) . |
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Due to the non-linear and non-stationary nature of daily new 2019 coronavirus disease (COVID-19) case time series, existing prediction methods struggle to accurately forecast the number of daily new cases. To address this problem, a hybrid prediction framework is proposed in this study, which combines ensemble empirical mode decomposition (EEMD), fuzzy entropy (FE) reconstruction, and a CNN-LSTM-ATT hybrid network model. This new framework, named EEMD-FE-CNN-LSTM-ATT, is applied to predict the number of daily new COVID-19 cases. This study focuses on the daily new case dataset from the United States as the research subject to validate the feasibility of the proposed prediction framework. The results show that EEMD-FE-CNN-LSTM-ATT outperforms other baseline models in all evaluation metrics, demonstrating its efficacy in handling the non-linear and non-stationary epidemic time series. Furthermore, the generalizability of the proposed hybrid framework is validated on datasets from France and Russia. The proposed hybrid framework offers a new approach for predicting the COVID-19 pandemic, providing important technical support for future infectious disease forecasting.
Keyword ：
COVID-19 COVID-19 ensemble empirical mode decomposition ensemble empirical mode decomposition ensemble prediction ensemble prediction fuzzy entropy fuzzy entropy LSTM network LSTM network
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| GB/T 7714 | Ke, Wenhui , Lu, Yimin . Ensemble Prediction Method Based on Decomposition-Reconstitution-Integration for COVID-19 Outbreak Prediction [J]. | MATHEMATICS , 2024 , 12 (3) . |
| MLA | Ke, Wenhui 等. "Ensemble Prediction Method Based on Decomposition-Reconstitution-Integration for COVID-19 Outbreak Prediction" . | MATHEMATICS 12 . 3 (2024) . |
| APA | Ke, Wenhui , Lu, Yimin . Ensemble Prediction Method Based on Decomposition-Reconstitution-Integration for COVID-19 Outbreak Prediction . | MATHEMATICS , 2024 , 12 (3) . |
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| GB/T 7714 | Yue, Tianxiang , Wu, Chenchen , Shi, Wenjiao et al. Progress in models for coupled human and natural systems [J]. | SCIENCE CHINA-EARTH SCIENCES , 2024 , 67 (11) : 3631-3637 . |
| MLA | Yue, Tianxiang et al. "Progress in models for coupled human and natural systems" . | SCIENCE CHINA-EARTH SCIENCES 67 . 11 (2024) : 3631-3637 . |
| APA | Yue, Tianxiang , Wu, Chenchen , Shi, Wenjiao , Tian, Yongzhong , Wang, Qing , Lu, Yimin et al. Progress in models for coupled human and natural systems . | SCIENCE CHINA-EARTH SCIENCES , 2024 , 67 (11) , 3631-3637 . |
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利用WorldView-2高分辨率遥感影像,提出一种SS-UNet网络模型用于筏式海水养殖区信息提取.在顾及筏式海水养殖区形状特征的情况下,SS-UNet模型在U-Net模型的"U"形架构上,引入SPM和SA模块.测试结果表明SS-UNet模型取得了最高的精度水平,其中MIoU和Kappa系数分别达到91.72％和0.912 3.并且该模型可以更加准确地提取筏式海水养殖区,遗漏和错误分类等现象出现较少.与U-Net模型相比,在增加极少模型复杂度的情况下,SS-UNet模型的MIoU和Kappa系数分别提升了 10.41％和0.126.结果表明,SS-UNet模型实现了在高分辨率遥感影像中近岸海域筏式海水养殖区提取的结果精度和提取性能的有效提升.
Keyword ：
SA模块 SA模块 SPM模块 SPM模块 SS-UNet模型 SS-UNet模型 筏式海水养殖 筏式海水养殖 高分辨率遥感影像 高分辨率遥感影像
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| GB/T 7714 | 刘靳 , 卢毅敏 , 郭向钟 . 一种基于高分辨率遥感影像的近岸筏式养殖区提取方法 [J]. | 福州大学学报（自然科学版） , 2024 , 52 (5) : 528-535 . |
| MLA | 刘靳 et al. "一种基于高分辨率遥感影像的近岸筏式养殖区提取方法" . | 福州大学学报（自然科学版） 52 . 5 (2024) : 528-535 . |
| APA | 刘靳 , 卢毅敏 , 郭向钟 . 一种基于高分辨率遥感影像的近岸筏式养殖区提取方法 . | 福州大学学报（自然科学版） , 2024 , 52 (5) , 528-535 . |
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在全球新型冠状病毒肺炎(COVID-19)疫情大规模爆发,中国疫情反复的背景下,本研究探究中国新冠肺炎疫情时空传播规律及疫情影响因素,以提升中国突发公共卫生事件应急能力.文章利用GIS可视化、空间自相关等方法分析新冠肺炎疫情时间过程与空间演变特征,并从经济、人口、社会、医疗等方面,运用Pearson相关性法和随机森林机器学习模型,系统分析疫情发展演变的主要影响因素,旨在为中国应对突发公共卫生事件提供研究基础和决策参考.研究结果表明:1)在时间上累计确诊病例数量呈现先迅速增长到平缓期再迅速增长的趋势,新增确诊数量呈现阶段性驼峰式变化.2)累计确诊病例的空间分布具有一定的空间自相关性.3)2021年中国新冠疫情的发展演变的影响因素中人口数量因素对2021年全国累计确
Keyword ：
中国 中国 影响因素 影响因素 新冠肺炎疫情 新冠肺炎疫情 时空演变 时空演变 空间自相关 空间自相关
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| GB/T 7714 | 刘庆 , 卢毅敏 . 中国新冠疫情的时空传播规律及其影响因素 [J]. | 亚热带资源与环境学报 , 2024 , 19 (3) : 170-178 . |
| MLA | 刘庆 et al. "中国新冠疫情的时空传播规律及其影响因素" . | 亚热带资源与环境学报 19 . 3 (2024) : 170-178 . |
| APA | 刘庆 , 卢毅敏 . 中国新冠疫情的时空传播规律及其影响因素 . | 亚热带资源与环境学报 , 2024 , 19 (3) , 170-178 . |
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Aquaculture has experienced significant growth, contributing to resolving the global food crisis and delivering substantial economic benefits. Nevertheless, the uncontrolled expansion of aquaculture activities has led to an ecological crisis in offshore waters. This highlights the critical need for precise delineation and monitoring of aquaculture areas in these regions to ensure scientific management and sustainable development of coastal areas. In this article, we introduced an SRUNet model based on the Swin Transformer for accurately extracting offshore raft aquaculture areas using medium-resolution remote sensing images. Our SRUNet model combined the UNet model with the Swin Transformer block and the residual block to account for multiscale features, resulting in excellent extraction performance in diverse and complex sea areas. To evaluate the model, we selected four typical raft aquaculture areas and compared the SRUNet model with other comparative network models. Results revealed that the SRUNet model outperformed all other models, and the F1 Score and MIoU of the classification results were 86.52% and 87.22%, respectively. The model reduced the loss of feature information and misclassification of aquaculture areas, generating extraction effects that aligned closely with real aquaculture area shapes. Additionally, we tested the performance of each component of the SRUNet model. The results indicate that the SRUNet model exhibits strong robustness and effectively filters out irrelevant information. These results demonstrate the model's potential for large-scale extraction of offshore aquaculture areas.
Keyword ：
Aquaculture Aquaculture Biological system modeling Biological system modeling Feature extraction Feature extraction Optical imaging Optical imaging Optical reflection Optical reflection Optical sensors Optical sensors Raft aquaculture Raft aquaculture Remote sensing Remote sensing residual block residual block sentinel series satellites data sentinel series satellites data Swin Transformer Swin Transformer
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| GB/T 7714 | Liu, Jin , Lu, Yimin , Guo, Xiangzhong et al. A Deep Learning Method for Offshore Raft Aquaculture Extraction Based on Medium-Resolution Remote Sensing Images [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 : 6296-6309 . |
| MLA | Liu, Jin et al. "A Deep Learning Method for Offshore Raft Aquaculture Extraction Based on Medium-Resolution Remote Sensing Images" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16 (2023) : 6296-6309 . |
| APA | Liu, Jin , Lu, Yimin , Guo, Xiangzhong , Ke, Wenhui . A Deep Learning Method for Offshore Raft Aquaculture Extraction Based on Medium-Resolution Remote Sensing Images . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 , 6296-6309 . |
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Within the context of PM2.5 concentrations decreasing annually, ozone concentrations have increased instead of decreased, and ozone has become one of the main pollutants in the warm season in China. Based on the idea of big data association analysis, the extreme gradient boosting (XGBoost) ozone concentration estimation model was constructed and developed to estimate the maximum daily 8 h average ozone concentration (O3_8h) in China in 2019 for human exposure assessment. The model input ground monitoring station data, high-resolution remote-sensing satellite data, meteorological data, emission inventory data, digital elevation model (DEM) data, and population data were used to capture the temporal and spatial variation of O3_8h. In this study, ten-fold cross-validation was used to evaluate the estimation performance of the model (R2=0.871, RMSE=11.7 μg•m-3). Compared to those with the random forest (RF) model and kernel ridge regression (KRR) model, due to the improvement in the algorithm itself and the advancement of parallel processing, the estimation results of the XGBoost model showed higher accuracy (RF: R2=0.864, RMSE=12.387 μg•m-3). The KRR model was as follows: R2=0.582, RMSE=23.1 μg•m-3, and the computational efficiency of the model was significantly improved. At the same time, the level of ozone exposure and the relative risk of death due to chronic obstructive pulmonary disease (COPD) in China's provinces and cities were evaluated. The results showed that the top five number of days exceeding the standard occurred in Shandong Province, Henan Province, Hebei Province, Anhui Province, and the Ningxia Hui Autonomous Region. In terms of exposure intensity, Hebei Province, Shandong Province, Shanxi Province, Tianjin City, and Jiangsu Province ranked the top five in terms of population weighted ozone concentration. In terms of health effects, the relative risk of COPD death showed seasonal changes, with the highest in summer and the lowest in winter. © 2022, Science Press. All right reserved.
Keyword ：
Computational efficiency Computational efficiency Decision trees Decision trees Health risks Health risks Meteorology Meteorology Ozone Ozone Population statistics Population statistics Pulmonary diseases Pulmonary diseases Regression analysis Regression analysis Remote sensing Remote sensing
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| GB/T 7714 | Zhao, Nan , Lu, Yi-Min . Estimation of Surface Ozone Concentration and Health Impact Assessment in China [J]. | Environmental Science , 2022 , 43 (3) : 1235-1245 . |
| MLA | Zhao, Nan et al. "Estimation of Surface Ozone Concentration and Health Impact Assessment in China" . | Environmental Science 43 . 3 (2022) : 1235-1245 . |
| APA | Zhao, Nan , Lu, Yi-Min . Estimation of Surface Ozone Concentration and Health Impact Assessment in China . | Environmental Science , 2022 , 43 (3) , 1235-1245 . |
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It is important for aquaculture monitoring, scientific planning, and management to extract offshore aquaculture areas from medium-resolution remote sensing images. However, in medium-resolution images, the spectral characteristics of offshore aquaculture areas are complex, and the offshore land and seawater seriously interfere with the extraction of offshore aquaculture areas. On the other hand, in medium-resolution images, due to the relatively low image resolution, the boundaries between breeding areas are relatively fuzzy and are more likely to 'adhere' to each other. An improved U-Net model, including, in particular, an atrous spatial pyramid pooling (ASPP) structure and an up-sampling structure, is proposed for offshore aquaculture area extraction in this paper. The improved ASPP structure and up-sampling structure can better mine semantic information and location information, overcome the interference of other information in the image, and reduce 'adhesion'. Based on the northeast coast of Fujian Province Sentinel-2 Multispectral Scan Imaging (MSI) image data, the offshore aquaculture area extraction was studied. Based on the improved U-Net model, the F1 score and Mean Intersection over Union (MIoU) of the classification results were 83.75% and 73.75%, respectively. The results show that, compared with several common classification methods, the improved U-Net model has a better performance. This also shows that the improved U-Net model can significantly overcome the interference of irrelevant information, identify aquaculture areas, and significantly reduce edge adhesion of aquaculture areas.
Keyword ：
classification classification deep learning deep learning medium-resolution remote sensing image medium-resolution remote sensing image offshore aquaculture area offshore aquaculture area U-Net U-Net
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| GB/T 7714 | Lu, Yimin , Shao, Wei , Sun, Jie . Extraction of Offshore Aquaculture Areas from Medium-Resolution Remote Sensing Images Based on Deep Learning [J]. | REMOTE SENSING , 2021 , 13 (19) . |
| MLA | Lu, Yimin et al. "Extraction of Offshore Aquaculture Areas from Medium-Resolution Remote Sensing Images Based on Deep Learning" . | REMOTE SENSING 13 . 19 (2021) . |
| APA | Lu, Yimin , Shao, Wei , Sun, Jie . Extraction of Offshore Aquaculture Areas from Medium-Resolution Remote Sensing Images Based on Deep Learning . | REMOTE SENSING , 2021 , 13 (19) . |
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以泉州湾秀涂人工岛的建设为例,基于有限体积海岸海洋模型,建立泉州湾三维数值模型,模拟分析建岛前、后水动力特征、潮致余流和纳潮量的变化.采用欧拉弥散方法模拟污染物浓度的对流扩散,对泉州湾的水交换能力进行分析.结果表明:建岛后,大部分海域的海潮流速约减小0.1 m·s-1;石湖港区人工岛连线以西大部分区域的潮致余流变化不显著,但湾口的潮致余流出现较为明显的减少;纳潮量的变化较为明显,小潮期间纳潮量的变化率为10.09％,使小潮期间湾内水体与外海的交换能力变弱,更易遭受污染威胁;洛阳江流域和金屿的污染物浓度差变化较大,导致湾内水体的半交换时间约增加3 d.
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| GB/T 7714 | 谢毅晖 , 卢毅敏 . 有限体积海岸海洋模型模拟泉州湾人工岛建设对水交换的影响 [J]. | 华侨大学学报（自然科学版） , 2021 , 42 (3) : 369-377 . |
| MLA | 谢毅晖 et al. "有限体积海岸海洋模型模拟泉州湾人工岛建设对水交换的影响" . | 华侨大学学报（自然科学版） 42 . 3 (2021) : 369-377 . |
| APA | 谢毅晖 , 卢毅敏 . 有限体积海岸海洋模型模拟泉州湾人工岛建设对水交换的影响 . | 华侨大学学报（自然科学版） , 2021 , 42 (3) , 369-377 . |
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With the rapid development of economy, the problem of river pollution is becoming more and more serious. It is very important and challenging to build a high-precision water quality prediction model for the comprehensive management of water environment and prevention of water pollution. At present, some achievements have been made in data-driven river water quality prediction, mainly including grey model, time series model, support vector machine model and neural network model. However, these existing prediction methods are limited to single station prediction. The influence of the upstream water quality on the downstream water quality is ignored and the upstream stations at different locations will have different effects on the downstream water quality. In order to solve the problem that the traditional model of water quality prediction does not consider the upstream influence and difficulties in long-term prediction, a new water quality prediction model based on spatiotemporal attention mechanism and long-short-term memory neural network (TS-Attention-LSTM) was proposed, which considers the spatiotemporal correlation and meteorological factors. Firstly, the spatial attention module was embedded in the encoder to extract the significant spatial correlation between upstream and downstream. The interaction among the water quality indicators was also extracted. Then, the temporal attention module was embedded in the decoder to extract the important time series features. Moreover, the meteorological factors and spatial characteristics were fused in the decoder. Finally, the multi-step prediction of water quality was carried out by using LSTM model. In this paper, the research area located in Jinjiang River Watershed in Fujian Province, China. The nonpoint source pollution in this river basin mainly interrelated with livestock discharge, agricultural field runoff and rural domestic sewage. The results show that the TS-Attention-LSTM model can effectively capture the spatial and temporal characteristics of water quality index (such as dissolved oxygen, DO and total phosphorus, TP) and the influence of meteorological factors in Jinjiang River Basin. The mean absolute error (MAE) of the TS-Attention-LSTM model was 0.24, the mean absolute percent error (MAPE) was 3.36%, and the root mean square error (RMSE) was 0.32, which performed best in all comparison models. The determinable coefficient R2 was 0.5062, performed second best in all comparison models. © 2021 IEEE.
Keyword ：
Agricultural robots Agricultural robots Agricultural runoff Agricultural runoff Agriculture Agriculture Biochemical oxygen demand Biochemical oxygen demand Decoding Decoding Dissolved oxygen Dissolved oxygen Errors Errors Long short-term memory Long short-term memory Mean square error Mean square error Predictive analytics Predictive analytics River pollution River pollution Rivers Rivers Sewage Sewage Support vector machines Support vector machines Time series Time series Water management Water management Water quality Water quality Watersheds Watersheds Weather forecasting Weather forecasting
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| GB/T 7714 | Lu, Yi-Min , Zhang, Hong , Shao, Wei . Prediction of river water quality considering spatiotemporal correlation and meteorological factors [C] . 2021 . |
| MLA | Lu, Yi-Min et al. "Prediction of river water quality considering spatiotemporal correlation and meteorological factors" . (2021) . |
| APA | Lu, Yi-Min , Zhang, Hong , Shao, Wei . Prediction of river water quality considering spatiotemporal correlation and meteorological factors . (2021) . |
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