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International Journal of Digital Earth 2023年第1期16卷 3661-3679页

作者： Laura Melgar-García Francisco Martínez-álvarez Dieu Tien bui Alicia Troncoso Data Science&Big Data Lab Pablo de Olavide UniversitySevilleSpain GIS Group Department of Business and ITUniversity of South-Eastern NorwayGullbringvegen 36B?i Telemark3800Norway

Floods remain one of the most devastating weather-induced disastersworldwide, resulting in numerous fatalities each year and severelyimpacting socio-economic development and the ***, the ability to predict flood-prone areas in advance is crucialfor effective risk management. The objective of this research is to assessand compare three convolutional neural networks, U-Net, WU-Net, andU-Net++, for spatial prediction of pluvial flood with a case study at atropical area in the north of Vietnam. They are relative new convolutionalgorithms developed based on U-shaped architectures. For this task, ageospatial database with 796 historical flood locations and 12 floodindicators was prepared. For training the models, the binary crossentropywas employed as the loss function, while the Adaptive momentestimation (ADAM) algorithm was used for the optimization of themodel parameters, whereas, F1-score and classification accuracy (Acc)were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models,achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and informationto enhance decision-making processes and facilitate the implementation of effective risk management strategies.

关键词： Flash-flood assessment convolutional networks climate change GIS

Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network:A Case Study of Vietnam

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 2219-2241页

作者： Huong Thi Thanh Ngo Nguyen Duc Dam Quynh-Anh Thi bui Nadhir Al-Ansari Romulus Costache Hang Ha Quynh Duy bui Sy Hung Mai Indra Prakash Binh Thai Pham University of Transport Technology Hanoi100000Vietnam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Department of Civil Engineering Transilvania University of BrasovBrasov500152Romania Danube Delta National Institute for Research and Development Tulcea820112Romania Departement of Geodesy and Geomatics National University of Civil EngineeringHanoi100000Vietnam Faculty of Hydraulic Engineering National University of Civil EngineeringHanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of *** types of floods are mostly associated with landslides and erosion of roads within a short *** of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic *** this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,*** the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental *** performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.

关键词： Flash flood deep learning neural network(DL) machine learning(ML) receiver operating characteristic curve(ROC) Vietnam

Combining SnO_(2-x)and g-C_(3)N_(4)nanosheets toward S-scheme heterojunction for high selectivity into green products of NO degradation reaction under visible light

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Journal of Materiomics 2022年第1期8卷 1-8页

作者： Pham Van Viet Thanh-Dat Nguyen Dai-Phat bui Cao Minh Thi Faculty of Materials Science and Technology University of ScienceVNU-HCM227 Nguyen Van Cu StreetDistrict 5Ho Chi Minh City700000Viet Nam Vietnam National University-Ho Chi Minh City Thu Duc DistrictHo Chi Minh City700000Viet Nam Ho Chi Minh City University of Technology(HUTECH) 475A Dien Bien Phu StreetBinh Thanh DistrictHo Chi Minh City700000Viet Nam

NOx emissions cause many negative impacts on the living *** photocatalysis of semiconductors is superior for nitric oxide(NO)degradation due to their low redox *** this report,we combine SnO_(2-x)/g-C_(3)N_(4)heterojunction photocatalyst toward the high selectivity into green products under visible light *** show that SnO_(2-x)/g-C_(3)N_(4)heterojunction degraded 40.8%of NO,which is 1.6 times higher than that of g-C_(3)N_(4).In addition,the selectivity coefficient of SnO_(2-x)/g-C_(3)N_(4)is higher 3 times than both pure SnO_(2-x)and g-C_(3)N_(4).Furthermore,SnO_(2-x)/g-C_(3)N_(4)expresses a superior stability for NO photocatalytic-degradation after five *** scavenger trapping test results,and electron spin resonance(ESR)analysis also provide more understanding of the charge transfer mechanism of ***_(2-x)/g-C_(3)N_(4)heterojunction shows a high removal efficiency of NO gas,making it an up-and-coming environmental treatment candidate.

关键词： G-C_(3)N_(4) NO removal Oxygen vacancies Photocatalysis S-scheme

Buckling Analysis of FG Porous Truncated Conical Shells Resting on Elastic Foundations in the Framework of the Shear Deformation Theory

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Advances in Applied Mathematics and Mechanics 2022年第1期14卷 218-247页

作者： Le Kha Hoa bui Gia Phi Do Quang Chan Dang Van Hieu Military Academy of Logistics HanoiVietnam Department of Technical Fundamental University of Transport Technology54 Trieu Khuc9 Thanh XuanHanoiVietnam Department of Mechanical Engineering and Mechatronics PHENIKAA UniversityHanoi 12116Vietnam PHENIKAA Research and Technology Institute(PRATI) A&A Green Phoenix Group JSCNo.167 Hoang NganTrung HoaCau GiayHanoi 11313Vietnam Thainguyen University of Technology ThainguyenVietnam

In this article,an analytical method is proposed to analyze of the linear buckling behavior of the FG porous truncated conical shells subjected to a uniform axial compressive load and resting on the Pasternak elastic *** material properties including Young’s modulus,shear modulus and density are assumed to vary in the thickness *** types of FG porous distributions including symmetric porosity distribution,non-symmetric porosity and uniform porosity distribution are *** governing equations of the FG porous truncated conical shells are obtained by using the first-order shear deformation theory(FSDT).With the help of the Galerkin method,the expressions for critical buckling loads are obtained in closed *** reliability of the obtained results is verified by comparing the present solutions with the published ***,the numerical results show the effects of shell characteristics,porosity distribution,porosity coefficient,and elastic foundation on the critical buckling load.

关键词： Buckling truncated conical shells first order shear deformation theory porous materials elastic foundations

Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers

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International Journal of Sediment Research 2023年第1期38卷 128-140页

作者： Duong Tran Anh Ahad Hasan Tanim Daniel Prakash Kushwaha Quoc Bao Pham Van Hieu bui HUTECH University Center for River Harbor and Landslide Research Chittagong University of Engineering & Technology Govind Ballabh Pant University of Agriculture and Technology Institute of Applied Technology Thu Dau Mot University Faculty of Mechanical Engineering Thuyloi University

The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution(GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory(LSTM) combined with the Discrete Element Method(DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in threedimensional(3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination(R~2), root mean squared error(RMSE), and mean absolute error(MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples.

关键词： Numerical modeling Discrete element method LSTM Bed porosity Gravel-bed river

Dynamics modeling and motion control for high-speed underwater vehicles using H-infinity synthesis with anti-windup compensator

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Journal of Ocean Engineering and Science 2022年第1期7卷 84-91页

作者： Phuc Duc Hong bui Sam-Sang You Hwan-Seong Kim Sang-Do Lee Department of Mechanical Engineering The University of TulsaTulsaOK 74104United States Division of Mechanical Engineering Korea Maritime and Ocean UniversityBusan 49112Republic of Korea Division of Logistics Engineering Korea Maritime and Ocean UniversityBusan 49112Republic of Korea Division of Navigation and Information System Mokpo National Maritime UniversityMokpoJeollanam-do 58628Republic of Korea

The control synthesis of a high-speed supercavitating vehicle(HSSV)faces many difficulties such as the stability,control,and maneuvering with dynamical uncertainties due to parameter perturbations,exter-nal disturbances,unmodeled dynamics,measurement noises,and actuator *** by the HSSV dynamical analysis,this paper proposes the H∞(i.e."H-infinity")robust control synthesis to gen-erate a robust low-order controller,which is intended for real implementations to ensure active control ***,the presented control scheme includes a feedback component and an anti-windup *** anti-windup synthesis is to provide system stability under actuator ***-tensive simulations show that the designed controller provides good performances with high robustness for vertical plane manoeuver while effectively eliminating planning forces,exogenous disturbances and noises as well as overcoming cavitator saturations.

关键词： Vehicle dynamics Planing force Vertical maneuver H-infinity control Anti-windup Actuator saturation

Stochastic Systems Arising from Markov Modulated Empirical Measures

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Journal of Systems Science & Complexity 2017年第5期30卷 999-1011页

作者： YIN Gang bui Trang Department of Mathematics Wayne State UniversityDetroitMI 48202USA

This work is devoted to stochastic systems arising from empirical measures of random sequences(termed primary sequences) that are modulated by another Markov chain. The Markov chain is used to model random discrete events that are not represented in the primary sequences. One novel feature is that in lieu of the usual scaling in empirical measure sequences, the authors consider scaling in both space and time, which leads to new limit results. Under broad conditions, it is shown that a scaled sequence of the empirical measure converges weakly to a number of Brownian bridges modulated by a continuous-time Markov chain. Ramifications and special cases are also considered.

关键词：随机系统连续时间马尔可夫链随机序列经验测度离散事件弱收敛调制缩放

Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

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Pedosphere 2022年第5期32卷 718-732页

作者： Khabat KHOSRAVI Phuong T.T.NGO Rahim BARZEGAR John QUILTY Mohammad T.AALAMI Dieu T.bui Department of Watershed Management Engineering Ferdowsi University of MashhadMashhad 93(Iran) Department of Earth and Environment Florida International UniversityMiami 33199(USA) Institute of Research and Development Duy Tan UniversityDa Nang 550000(Vietnam) Department of Bioresource Engineering McGill UniversitySte Anne de Bellevue QC H9X(Canada) Faculty of Civil Engineering University of TabrizTabriz 51(Iran) Department of Civil and Environmental Engineering University of WaterlooWaterloo N2L 3G1(Canada) Department of Business and IT University of South-Eastern NorwayNotodden 3603(Norway)

Water infiltration into soil is an important process in hydrologic cycle;however,its measurement is difficult,time-consuming and *** and physical models have been developed to predict cumulative infiltration(CI),but are often *** this study,several novel standalone machine learning algorithms(M5Prime(M5P),decision stump(DS),and sequential minimal optimization(SMO))and hybrid algorithms based on additive regression(AR)(i.e.,AR-M5P,AR-DS,and AR-SMO)and weighted instance handler wrapper(WIHW)(i.e.,WIHW-M5P,WIHW-DS,and WIHW-SMO)were developed for CI *** Soil Conservation Service(SCS)model developed by the United States Department of Agriculture(USDA),one of the most popular empirical models to predict CI,was considered as a ***,154 measurements of CI(explanatory/input variables)were taken from 16 sites in a semi-arid region of Iran(Illam and Lorestan provinces).Six input variable combinations were considered based on Pearson correlations between candidate model inputs(time of measuring and soil bulk density,moisture content,and sand,clay,and silt percentages)and *** dataset was divided into two subgroups at random:70%of the data were used for model building(training dataset)and the remaining 30%were used for model validation(testing dataset).The various models were evaluated using different graphical approaches(bar charts,scatter plots,violin plots,and Taylor diagrams)and quantitative measures(root mean square error(RMSE),mean absolute error(MAE),Nash-Sutcliffe efficiency(NSE),and percent bias(PBIAS)).Time of measuring had the highest correlation with CI in the study *** best input combinations were different for different *** results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone *** AR-M5P model provided the most accurate CI predictions(RMSE=0.75 cm,MAE=0.59 cm,NSE=0.98),while the SCS model had the lowest performance(RMSE=4.77 cm,MAE=2.64 cm,NSE=

关键词： additive regression hybrid algorithms empirical model soil water infiltration weighted instances handler wrapper

Atomistic simulation of free transverse vibration of graphene,hexagonal SiC, and BN nanosheets

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Acta Mechanica Sinica 2017年第1期33卷 132-147页

作者： Danh-Truong Nguyen Minh-Quy Le Thanh-Lam bui Hai-Le bui School of Mechanical Engineering Hanoi University of Science and Technology Hanoi 10000 Vietnam International Institute for Computational Science and Engineering Hanoi University of Science and TechnologyHanoi 10000 Vietnam Faculty of Mechanical Engineering Hanoi University of Industry Hanoi 10000 Vietnam

Free transverse vibration of monolayer graphene, boron nitride (BN), and silicon carbide (SiC) sheets is investigated by using molecular dynamics finite element method. Eigenfrequencies and eigenmodes of these three sheets in rectangular shape are studied with different aspect ratios with respect to various boundary conditions. It is found that aspect ratios and boundary conditions affect in a similar way on natural frequencies of graphene, BN, and SiC sheets. Natural frequencies in all modes decrease with an increase of the sheet’s size. Graphene exhibits the highest natural frequencies, and SiC sheet possesses the lowest ones. Missing atoms have minor effects on natural frequencies in this study.

关键词： Atomistic simulation Hexagonal sheet Transverse vibration Molecular dynamics finite element method

Improvement of a temperature response function for divertor heat flux monitoring in fusion devices

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Plasma Science and Technology 2023年第3期25卷 217-221页

作者： Xuan Nhat Son bui Hiroto MATSUURA Yousuke NAKASHIMA Quantum and Radiation Engineering Department Osaka Prefecture University Plasma Research Center University of Tsukuba

Temperature response functions have been developed to investigate sensor design and divertor heat flux estimation in magnetically confined plasmas. The time-dependent heat flux can be derived by fitting the response function to experimental thermocouple(TC) data. Because the TC signals have a time delay to transit events such as discharge start or confinement transition, the time delay is taken into account in a temperature response function. Such a function accurately describes the signal from each TC channel with time delay in a sensor test using a neutral beam injection. Measurement for commercial TCs shows that the time delay is caused by the finite heat capacity of TC wire and contact heat resistance between TC and target surface.

关键词： divertor temperature response function thermocouple system primary delay