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学者姓名：张挺
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为准确获取山区小流域的降水空间分布及其资源量, 采用Kriging插值法对低分辨率卫星数据进行空间降尺度处理, 通过长短期记忆网络(Long Short-Term Memory, LSTM)将局部卫星与观测数据进行降水融合, 引入前期降水信息加强卫星与观测降水之间的时间相关性, 并利用该模型进行流域降水空间分布估计。结果表明: 从空间分布来看, 融合模型对暴雨中心位置的捕捉更加精确; 从降水量来看, 所提模型在短时强降水下的探测率和临界成功指数分别为0.60和0.50, 能够改善原始低分辨率卫星降水数据, 使其更接近实际情况; 从雨量站数量来看, 融合降水的精度随着站点数量的增加而提高, 当站点数量达到某个临界值时, 融合降水的精度趋于稳定。Kriging-LSTM模型为准确获取山区小流域的降水资源提供了新思路。
Keyword ：
Kriging插值法 Kriging插值法 山区小流域 山区小流域 长短期记忆网络(LSTM) 长短期记忆网络(LSTM) 降水空间估计 降水空间估计 降水融合 降水融合
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| GB/T 7714 | 詹昌洵 , 张挺 , 蒋嘉伟 . 基于多源数据的山区小流域降水融合模型 [J]. | 水科学进展 , 2024 , 35 (1) : 74-84 . |
| MLA | 詹昌洵 等. "基于多源数据的山区小流域降水融合模型" . | 水科学进展 35 . 1 (2024) : 74-84 . |
| APA | 詹昌洵 , 张挺 , 蒋嘉伟 . 基于多源数据的山区小流域降水融合模型 . | 水科学进展 , 2024 , 35 (1) , 74-84 . |
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In addressing the intricate dynamic responses of pipeline conveying fluid characterized by spatiotemporal multiscales and multi-modal contributions, Fourier feature-embedded physics-information neural network (FF-PINN) is proposed. By introducing Fourier feature mapping to decompose the temporal and spatial scale information, FF-PINN precisely captures the relatively low-frequencies on the macroscopic time scale as well as the relatively high-frequencies on the microscopic scale of the pipeline's vibration. This approach significantly overcomes the spectral bias encountered by PINN when learning high-frequency information. To verify the effectiveness and accuracy of this method, the proposed FF-PINN is applied to solve the pipeline conveying fluid model with fixed support at both ends. The relative L2 error between the obtained results and the reference solution is 1.8 x 10-2, concurrently with a significant reduction in computational time. Additionally, an analysis of hyperparameter sigma selection is conducted to evaluate its impact on the performance of FF-PINN, while establishing the correspondence between hyperparameter and eigenvector frequency. The results demonstrate that choosing appropriate hyperparameters empowers FF-PINN to better learn the vibration of specific frequencies, enabling the accurate modeling of pipeline vibrations' dynamic response. It provides a potent solution for solving spatiotemporal multi-scale complexity problems involving the superposition of high-and low-frequencies.
Keyword ：
Fourier feature Fourier feature Physics-information neural network Physics-information neural network Pipeline conveying fluid Pipeline conveying fluid Spatiotemporal multi-scales Spatiotemporal multi-scales Vibration characteristics Vibration characteristics
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| GB/T 7714 | Zhang, Ting , Yan, Rui , Zhang, Siqian et al. Application of Fourier feature physics-information neural network in model of pipeline conveying fluid [J]. | THIN-WALLED STRUCTURES , 2024 , 198 . |
| MLA | Zhang, Ting et al. "Application of Fourier feature physics-information neural network in model of pipeline conveying fluid" . | THIN-WALLED STRUCTURES 198 (2024) . |
| APA | Zhang, Ting , Yan, Rui , Zhang, Siqian , Yang, Dingying , Chen, Anhao . Application of Fourier feature physics-information neural network in model of pipeline conveying fluid . | THIN-WALLED STRUCTURES , 2024 , 198 . |
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Escalation in flash floods and the enhanced devastations, especially in the arid and semiarid regions of the world has required precise mapping of the flash flood susceptible zones. In this study, we applied six novel credal decision tree (CDT)-based ensemble models-1. CDT, 2. CDT Alternative Decision Tree (ADTree), 3. CDT- Reduced Error Pruning Tree (REPT), 4. CDT- Rotational Forest (RF), 5. CDT-FT, 6. CDT- Naive Bias Tree (NBTree). For preparing the flash flood susceptibility maps (FFSM), 206 flood locations were selected in the Neka-roud watershed of Iran with 70% as training data and 30% as testing data. Moreover, 18 flood conditing factors were considered for FFSM and a multi-colinearity test was performed for determining the role of the factors. Our results show that the distance from the stream plays a vital role in flash floods. The CDT-FT is the best-fit model out of the six novel algorithms employed in this study as demonstrated by the highest values of the area under the curve (AUC) of the receiver operating curve (ROC) (AUROC 0.986 for training data and 0.981 for testing data). Our study provides a novel approach and useful tool for flood management.
Keyword ：
Credal decision tree Credal decision tree Flash flood mapping Flash flood mapping Flood management Flood management Machine learning algorithms Machine learning algorithms Neka-roud watershed Neka-roud watershed Novel Ensemble models Novel Ensemble models
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| GB/T 7714 | Yang, Dingying , Zhang, Ting , Arabameri, Alireza et al. Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches [J]. | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) : 3143-3161 . |
| MLA | Yang, Dingying et al. "Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches" . | EARTH SCIENCE INFORMATICS 16 . 4 (2023) : 3143-3161 . |
| APA | Yang, Dingying , Zhang, Ting , Arabameri, Alireza , Santosh, M. , Saha, Ujwal Deep , Islam, Aznarul . Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches . | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) , 3143-3161 . |
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Abstract ：
Escalation in flash floods and the enhanced devastations, especially in the arid and semiarid regions of the world has required precise mapping of the flash flood susceptible zones. In this study, we applied six novel credal decision tree (CDT)-based ensemble models-1. CDT, 2. CDT Alternative Decision Tree (ADTree), 3. CDT- Reduced Error Pruning Tree (REPT), 4. CDT- Rotational Forest (RF), 5. CDT-FT, 6. CDT- Naive Bias Tree (NBTree). For preparing the flash flood susceptibility maps (FFSM), 206 flood locations were selected in the Neka-roud watershed of Iran with 70% as training data and 30% as testing data. Moreover, 18 flood conditing factors were considered for FFSM and a multi-colinearity test was performed for determining the role of the factors. Our results show that the distance from the stream plays a vital role in flash floods. The CDT-FT is the best-fit model out of the six novel algorithms employed in this study as demonstrated by the highest values of the area under the curve (AUC) of the receiver operating curve (ROC) (AUROC 0.986 for training data and 0.981 for testing data). Our study provides a novel approach and useful tool for flood management.
Keyword ：
Credal decision tree Credal decision tree Flash flood mapping Flash flood mapping Flood management Flood management Machine learning algorithms Machine learning algorithms Neka-roud watershed Neka-roud watershed Novel Ensemble models Novel Ensemble models
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| GB/T 7714 | Yang, Dingying , Zhang, Ting , Arabameri, Alireza et al. Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches [J]. | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) : 3143-3161 . |
| MLA | Yang, Dingying et al. "Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches" . | EARTH SCIENCE INFORMATICS 16 . 4 (2023) : 3143-3161 . |
| APA | Yang, Dingying , Zhang, Ting , Arabameri, Alireza , Santosh, M. , Saha, Ujwal Deep , Islam, Aznarul . Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches . | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) , 3143-3161 . |
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This research proposed an integrated strategy for building performance optimization from the whole life cycle perspective to explore the optimal building scheme. After the feature elimination, the ensemble learning model (ELM) was trained to obtain a high-precision model for predicting life cycle carbon emissions (LCCE), life cycle costs (LCC), and indoor discomfort hours (IDH). Then, the optimal optimization algorithm was selected among three different optimization algorithms. Finally, the best building scheme was chosen according to the newly proposed solution. The results showed that the ELM could achieve high prediction efficiency by combining input feature evaluation and screening, multi-sampling methods, and hyperparameter optimization. The R2 value of ELM can reach 0.980, while the Two-Archive Evolutionary Algorithm for Constrained multi-objective optimi-zation (C-TAEA) was the optimal optimization algorithm. The best equilibrium solution proposed in this study solved the problem of different optimization ranges of different objectives and maximized the optimization value. Finally, the best equilibrium scheme reduced the LCCE by 34.7%, the LCC by 13.9%, and the IDH by 26.6%. Therefore, this strategy can efficiently optimize building objectives and produce a more balanced and optimal building scheme, thus making it widely applicable in building performance optimization.
Keyword ：
Best equilibrium solution Best equilibrium solution Building performance optimization Building performance optimization Carbon emission Carbon emission Machine learning Machine learning Sensitivity analysis Sensitivity analysis
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| GB/T 7714 | Chen, Ruijun , Tsay, Yaw-Shyan , Zhang, Ting . A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective [J]. | ENERGY , 2023 , 262 . |
| MLA | Chen, Ruijun et al. "A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective" . | ENERGY 262 (2023) . |
| APA | Chen, Ruijun , Tsay, Yaw-Shyan , Zhang, Ting . A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective . | ENERGY , 2023 , 262 . |
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The semiconductors have created a great avenue in visible-light photocatalysis and recently insulator photocatalysis has become an appealing research spot. Herein, a novel waste eggshells derived AgBr-CaCO3 heterostructure was finely designed and constructed through a simple co-precipitation method for efficient antibiotics photo-degradation under visible light. The optimal heterostructure achieved a pseudo-first-order kinetic constant of 6.0 x 10(-2) min(-1) for tetracycline (TC) degradation, with 72 and seven-fold enhancement than eggshell (ES) and AgBr, which also exhibited superior performance towards ofloxacin and sulfamethoxazole. The density functional theory (DFT) calculations revealed that the covalent interaction of Ag-O provided a specific channel for interfacial electrons transfer from the semiconductor to the insulator and thus greatly elevated the photocatalytic activity. The highly selective .CO3- radicals generated by ES, as the main active species, also accelerated the antibiotics degradation. Furthermore, the possible degradation pathways, aquatic toxicity and mutagenicity variation of TC were thoroughly elucidated. This current study illuminated a new pathway for the design of insulator photocatalysts based upon waste solids and demonstrated its application prospect in the field of antibiotics degradation.
Keyword ：
AgBr AgBr CaCO3 CaCO3 Heterostructure Heterostructure Tetracycline Tetracycline Visible -light photocatalysis Visible -light photocatalysis
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| GB/T 7714 | Chen, Qiaoshan , Gao, Ming , Yu, Mingfei et al. Efficient photo-degradation of antibiotics by waste eggshells derived AgBr-CaCO3 heterostructure under visible light [J]. | SEPARATION AND PURIFICATION TECHNOLOGY , 2023 , 314 . |
| MLA | Chen, Qiaoshan et al. "Efficient photo-degradation of antibiotics by waste eggshells derived AgBr-CaCO3 heterostructure under visible light" . | SEPARATION AND PURIFICATION TECHNOLOGY 314 (2023) . |
| APA | Chen, Qiaoshan , Gao, Ming , Yu, Mingfei , Zhang, Ting , Wang, Jianchun , Bi, Jinhong et al. Efficient photo-degradation of antibiotics by waste eggshells derived AgBr-CaCO3 heterostructure under visible light . | SEPARATION AND PURIFICATION TECHNOLOGY , 2023 , 314 . |
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The dynamic responses of offshore pipelines induced by J-laying effects are remarkable and dominate the pipeline design and installation feasibility in practice. Most previous studies have focused on the analysis of pipeline installation on the horizontal seabed. In this paper, an extended J-lay model based on the vector mechanics principle is developed to investigate dynamic behaviors of laying pipelines on the sloping seabed. Two representative seabed slope types are taken into account involving the positive and negative slopes. The laying pipeline is divided into a sequence of mass particles linked by massless elements, and the deformations of the elements and the rotations of the particles are calculated to obtain the internal forces. The explicit central difference technique is applied to solve the movement governing equations of pipeline particles by program coding. Two illustrative cases of a 12-inch pipeline being laid separately from the horizontal seabed to the positive and negative sloping seabed are simulated under a random sea state. The influences of seabed slope type and angle on pipeline responses are estimated quantitatively. Significant differences of pipeline behaviors between the positive and negative sloping seabed are observed, which offer very intuitive evidences of seabed slope effects concerning deepwater J-laying pipelines.
Keyword ：
Deepwater Deepwater J-lay J-lay Offshore pipeline Offshore pipeline Sloping seabed Sloping seabed Vector form intrinsic finite element (VFIFE) Vector form intrinsic finite element (VFIFE)
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| GB/T 7714 | Xu, Pu , Du, Zhixin , Zhang, Ting et al. Vector form intrinsic finite element analysis of deepwater J-laying pipelines on sloping seabed [J]. | OCEAN ENGINEERING , 2022 , 247 . |
| MLA | Xu, Pu et al. "Vector form intrinsic finite element analysis of deepwater J-laying pipelines on sloping seabed" . | OCEAN ENGINEERING 247 (2022) . |
| APA | Xu, Pu , Du, Zhixin , Zhang, Ting , Chen, Baochun . Vector form intrinsic finite element analysis of deepwater J-laying pipelines on sloping seabed . | OCEAN ENGINEERING , 2022 , 247 . |
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Deformations in dam structures can have a critical impact on dam safety and life. Accurate methods for dam deformation prediction and safety evaluation are thus highly needed. Dam deformations can be predicted based on many factors. The analysis of these influences on the deformation of the dam reveals a problem that deserves further attention: dam deformation lags behind environmental factors of the water level and temperature as well as the time lag of the temporal dam deformation data. In this paper, a hybrid deep learning model is proposed to enhance the accuracy of dam deformation forecasting based on lag indices of these factors. In particular, dam deformations are predicted using deep networks based on gated recurrent units (GRUs), which can effectively capture the temporal characteristics of dam deformation. In addition, an improved particle swarm optimization (IPSO) algorithm is used for optimizing the GRU hyperparameters. Furthermore, the complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and the partial autocorrelation function (PACF) are exploited to select the lag factor indices. The accuracy and effectiveness of the proposed CEEMDAN-PACF-IPSO-GRU hybrid model were evaluated and compared with those of other existing models in terms of four different evaluation indices (MAE, MSE, R-2, and RMSE) and using 9-year historical data for the case of a pulp-masonry arch dam in China. The experimental results show that our model outperforms other models in terms of the deformation prediction accuracy (R-2 increased by 0.16%-9.74%, while the other indices increased by 14.55% to reach 96.69%), and hence represents a promising framework for general analysis of dam deformations and other types of structural behavior. (C) 2022 American Society of Civil Engineers.
Keyword ：
Complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) Complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) Dam deformation Dam deformation Gated recurrent unit (GRU) Gated recurrent unit (GRU) Improved particle swarm optimization (IPSO) Improved particle swarm optimization (IPSO) Partial autocorrelation function (PACF) Partial autocorrelation function (PACF)
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| GB/T 7714 | Lin, Chuan , Wang, Xiangyu , Su, Yan et al. Deformation Forecasting of Pulp-Masonry Arch Dams via a Hybrid Model Based on CEEMDAN Considering the Lag of Influencing Factors [J]. | JOURNAL OF STRUCTURAL ENGINEERING , 2022 , 148 (7) . |
| MLA | Lin, Chuan et al. "Deformation Forecasting of Pulp-Masonry Arch Dams via a Hybrid Model Based on CEEMDAN Considering the Lag of Influencing Factors" . | JOURNAL OF STRUCTURAL ENGINEERING 148 . 7 (2022) . |
| APA | Lin, Chuan , Wang, Xiangyu , Su, Yan , Zhang, Ting , Lin, Chaoning . Deformation Forecasting of Pulp-Masonry Arch Dams via a Hybrid Model Based on CEEMDAN Considering the Lag of Influencing Factors . | JOURNAL OF STRUCTURAL ENGINEERING , 2022 , 148 (7) . |
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| GB/T 7714 | Zhang, Ting , Lin, Zhen-Huan , Lin, Chuan et al. Numerical Simulation of the Time-Dependent Mild-Slope Equation by the Generalized Finite Difference Method (vol 178, pg 4401, 2021) [J]. | PURE AND APPLIED GEOPHYSICS , 2022 , 179 (2) : 897-897 . |
| MLA | Zhang, Ting et al. "Numerical Simulation of the Time-Dependent Mild-Slope Equation by the Generalized Finite Difference Method (vol 178, pg 4401, 2021)" . | PURE AND APPLIED GEOPHYSICS 179 . 2 (2022) : 897-897 . |
| APA | Zhang, Ting , Lin, Zhen-Huan , Lin, Chuan , Liang, Lin , Fan, Chia-Ming . Numerical Simulation of the Time-Dependent Mild-Slope Equation by the Generalized Finite Difference Method (vol 178, pg 4401, 2021) . | PURE AND APPLIED GEOPHYSICS , 2022 , 179 (2) , 897-897 . |
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本实用新型提出一种含变坡海床模型的波流水槽铺管动力研究试验装置，其特征在于：在波流水槽(1)中的动床试验段(2)设有变坡海床模型(3)，所述变坡海床模型(3)上铺设有悬链线管道(4)，所述悬链线管道(4)位于变坡海床模型(3)上的外表面设有若干组位移传感器(5)和电阻应变片(6)，顶端连接三维驱动装置(7)。目的在于有效模拟铺设管道与不同型式斜坡海床的动力相互作用。
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| GB/T 7714 | 徐普 , 杜志新 , 张挺 . 含变坡海床模型的波流水槽铺管动力研究试验装置 : CN202220902344.1[P]. | 2022-04-19 . |
| MLA | 徐普 et al. "含变坡海床模型的波流水槽铺管动力研究试验装置" : CN202220902344.1. | 2022-04-19 . |
| APA | 徐普 , 杜志新 , 张挺 . 含变坡海床模型的波流水槽铺管动力研究试验装置 : CN202220902344.1. | 2022-04-19 . |
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