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Improved knowledge-based neural network(KBNN)model for predicting spring-back angles in metal sheet bending

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International Journal of Modeling, Simulation, and Scientific Computing 2014年第2期5卷 65-91页

作者： Tinh Quoc bui Anh Viet Tran Abid Ali Shah Department of Civil Engineering University of Siegen Paul-Bontz-Str.9-1157076 SiegenGermany School of Mechanical and Automotive Engineering University of Ulsan San 29Mugeo 2-dong NamguP.O.Box 18UlsanSouth Korea

We develop an efficiently improved knowledge-based neural network(KBNN)associated with optimization algorithms and finite element analysis(FEA)to accurately predict spring-back angles in metal sheet *** well-known V and U prevalent processes of bending are *** KBNN predictive results are based on the empirical model and artificial neural network(ANN)*** empirical model is constructed from the FEA results using response surface method,while the multilayer perceptron is employed to create the *** trained KBNN can accurately model the relation-ship between the spring-back angles and process *** obtained results are validated against other existing methods showing a high accuracy.

关键词： Metal sheet bending spring-back angles knowledge-based neural network genetic algorithm optimization algorithm

Highly efficient vanadium redox flow batteries enabled by a trilayer polybenzimidazole membrane assembly

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Carbon Energy 2024年第7期6卷 191-204页

作者： Trung Tuyen bui Mingyu Shin Mohammad Rahimi Anders Bentien Yongchai Kwon Dirk Henkensmeier Hydrogen·Fuel Cell Research Center Korea Institute of Science and Technology(KIST)SeoulRepublic of Korea Department of Chemical and Biomolecular Engineering Seoul National University of Science and TechnologySeoulRepublic of Korea Department of Biological and Chemical Engineering Aarhus UniversityAarhusDenmark KIST School Division of Energy and Environment TechnologyUniversity of Science and TechnologySeoulRepublic of Korea Graduate School of Energy and Environment Korea UniversitySeoulRepublic of Korea

A novel polybenzimidazole(PBI)-based trilayer membrane assembly is developed for application in vanadium redox flow battery(VRFB).The membrane comprises a 1μm thin cross-linked poly[2,2′-(p-oxydiphenylene)−5,5′-bibenzimidazole](OPBI)sandwiched between two 20μm thick porous OPBI membranes(p-OPBI)without further lamination *** trilayer membrane demonstrates exceptional properties,such as high conductivity and low area-specific resistance(ASR)of 51 mS cm^(−1) and 81mΩ cm^(2),*** with vanadium electrolyte increases the ASR of trilayer membrane only to 158mΩ cm^(2),while that of Nafion is 193mΩ cm^(2).VO^(2+) permeability is 2.73×10^(-9) cm^(2) min^(−1),about 150 times lower than that of Nafion *** addition,the membrane has high mechanical strength and high chemical stability against VO^(2+).In VRFB,the combination of low resistance and low vanadium permeability results in excellent performance,revealing high Coulombic efficiency(>99%),high energy efficiency(EE;90.8% at current density of 80mA cm^(−2)),and long-term *** EE is one of the best reported to date.

关键词： polybenzimidazole porous membrane proton conductivity trilayer VRFBs

Imperfect Channel State Information of AF and DF Energy Harvesting Cooperative Networks

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China Communications 2016年第10期13卷 11-19页

作者： Hoang-Sy NGUYEN Anh-Hoa bui Dinh-Thuan DO Miroslav VOZNAK Wireless Communications Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang UniversityHo Chi Minh City Vietnam Eastern International University Binh Duong Province Vietnam Faculty of Electrical Engineering and Computer Science Technical University of Ostrava17. Listopadu 15/2172 70833 Ostrava Czech Republic

Wireless information and powered transfer networks(WIPT) has recently been implemented in 5th generation wireless networks. In this paper, we consider half-duplex relaying system in which the energy constrained relay node collects energy via radio frequency(RF) signals from the surrounding resources. Regarding energy harvesting protocol, we propose power time switching-based relaying(PTSR) architecture for both amplify-and-forward(AF) and decode-and-forward(DF). Especially, we reveal the analytical expressions of achievable throughput, ergodic capacity and energy-efficient in case of imperfect channel state information(CSI) for both AF and DF network. Through numerical analysis, we analyse the throughput performance, energy-efficient and ergodic capacity for different parameters, including power splitting ratio and energy harvesting time. Moreover, we also depict the performance comparison between AF and DF network with perfect and imperfect CSI. The results in numerical analysis reveal that the result of AF relaying network is less significant than DF relaying network in the various scenarios.

关键词： amplify-and-forward decode-and-forward energy harvesting throughput channel state information(CSI)

A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

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Intelligent Automation & Soft Computing 2023年第6期36卷 3029-3041页

作者： Minh Thanh Vo Anh HVo Huong bui Tuong Le Faculty of Information Technology HUTECH UniversityHo Chi Minh CityVietnam Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Computing Fundamentals FPT UniversityHo Chi Minh CityVietnam Laboratory for Artificial Intelligence Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Van Lang UniversityHo Chi Minh CityVietnam

Nowadays,air pollution is a big environmental problem in develop-ing *** this problem,particulate matter 2.5(PM2.5)in the air is an air *** its concentration in the air is high in developing countries like Vietnam,it will harm everyone’s *** prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the *** study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City,***,this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC *** variables that affect the results will be selected for PM2.5 concentration ***,an efficient PM25-CBL model that integrates a convolutional neural network(CNN)andBidirectionalLongShort-TermMemory(Bi-LSTM)*** model consists of three following modules:CNN,Bi-LSTM,and Fully connected ***,this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM,Bi-LSTM,the combination of CNN and LSTM(CNN-LSTM),and *** empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).

关键词： Time series prediction PM2.5 concentration prediction CNN Bi-LSTM network

Applying Molecular Breeding to Improve Soybean Rust Resistance in Vietnamese Elite Soybean

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American Journal of Plant Sciences 2013年第1期4卷 1-6页

作者： Tran Dang Khanh Truong Quoc Anh bui Chi Buu Tran Dang xuan 2Institute of Agricultural Science for Southern Vietnam Ho Chi Minh City Vietnam Department of Molecular Biology Agricultural Genetics Institute Hanoi Vietnam Graduate School for International Development and Cooperation Hiroshima University Hiroshima Japan Institute of Agricultural Science for Southern Vietnam Ho Chi Minh City Vietnam

Soybean production in Vietnam has recently been threatened by the widespread dissemination of soybean rust (SBR) caused by the fungus Phakopsora pachyrhizi. Application of molecular breeding is considered as a feasible method to improve soybean rust resistance and minimize the adverse effects from overuse fungicides in this country. In this study, we have successfully applied molecular markers in a backcross breeding program to introgress the Rpp5 gene of SBR resistance into HL203, an elite Vietnamese soybean variety, from two donor lines of DT2000 and Stuart 99084B-28. The plants in BC4F1 generation had maximum contribution from the recurrent parents and retained SBR resistance gene.

关键词： Rust Resistant Gene Soybean Marker Assisted Selection Molecular Marker

Mechanism of Japanese encephalitis virus genotypes replacement based on human,porcine and mosquito-originated cell lines model

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Asian Pacific Journal of Tropical Medicine 2016年第4期9卷 325-328页

作者： Loan Phuong Do Trang Minh bui Nga Thi Phan National Institute of Hygiene and Epidemiology

Objective:To examine the multiplication efficiency Japanese encephalitis virus(JEV)genotype Ⅰ(G Ⅰ) and genotype Ⅲ(GⅢ) of different cell lines which originated from human,porcine,mosquitoes in order to prove mechanism of JEV G Ⅰ replacement JEV GⅢ since it emerging in nature recent ***:The mixture of Gi and GⅢ JEV isolates was inoculated on human rhabdomyosarcoma(RD).pig kidney epithelial(PS) and Aedes albopictus C6/36 clone(C6/36) which originated from human,porcine and mosquitoes,*** assays were performed to calculate virus titer and real-time RT-PCR with GⅠand GⅢspecific primer sets to quantify the number of GⅠ and GUI RNA ***:The highest virus titer reached at the 3rd day of post infection when G Ⅰand GⅢ mixture was inoculated on RD and PS and that of C636 was at the 4^(th)*** were amplified and maintained by C6/36 cells after 10 passages whereas that by RD and PS only limited within 8 and 6 passages,respectively.GⅠ strain amplified and maintained more efficiently on C6/36 and PS but not *** GⅢ strain amplified and maintained more efficiently on ***:There is a correlation between the multiplication efficiency of GⅠ and GⅢ JEV strains when these two genotype strains co-infected on different cell lines with the predominance of GⅠstrains in C6/36 and PS and the limited detection of G 1 strains in RD cells proving a possible mechanism of shift JEV genotypes in nature recent decades since GⅠ emerging.

关键词： Japanese encephalitis virus Genotype I Genotype Ⅲ Multiplication Shift genotype

A novel semantic segmentation approach based on U-Net,WU-Net,and U-Net++deep learning for predicting areas sensitive to pluvial flood at tropical area

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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

＂RADIOTRANSCRIPTOMICS＂： A synergy of imaging and transcriptomics in clinical assess- ment

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Frontiers of Electrical and Electronic Engineering in China 2016年第1期4卷 1-12页

作者： Amal Katrib William Hsu Alex bui Yi Xing Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA_ 90095 USA Department of Radiological Sciences University of California Los Angeles Los Angeles CA 90095 USA

Recent advances in quantitative imaging and ＂omics＂ technology have generated a wealth of mineable biological ＂big data＂. With the push towards a P4 ＂predictive, preventive, personalized, and participatory＂ approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose ＂radiotranscriptomics＂ as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

关键词： quantitative imaging transcriptomics RNA-seq genomics image genomics radiogenomics systems biology precision medicine

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization:a case study in Central Vietnam

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International Journal of Digital Earth 2016年第11期9卷 1077-1097页

作者： Dieu Tien bui Binh Thai Pham Quoc Phi Nguyen Nhat-Duc Hoang Geographic Information System Group Department of Business Administration and Computer ScienceUniversity College of Southeast NorwayBøi TelemarkNorway Faculty of Geomatics and Land Administration Hanoi University of Mining and GeologyHanoiVietnam Department of Civil Engineering Gujarat Technological UniversityAhmedabadGujaratIndia Department of Geotechnical Engineering University of Transport TechnologyHa NoiVietNam Department of Environmental Sciences Hanoi University of Mining and GeologyHanoiVietnam Faculty of Civil Engineering Institute of Research and DevelopmentDuy Tan UniversityDanangVietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as *** LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM *** this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid *** receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed *** results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating *** model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision *** result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.

关键词： Shallow landslide Least-Squares Support Vector Machines differential evolution GIS Vietnam