检索结果-南通市图书馆

Patternable production of SrTiO_(3) nanoparticles using 1-W laser directly on flexible humidity sensor platform based on ITO/SrTiO_(3)/CNT

在线全文

学校读者我要写书评

暂无评论

Journal of Materials Science & Technology 2021年第12期71卷 186-194页

作者： le thai duy Ji-Ye Baek Ye-Ji Mun Hyungtak Seo Department of Materials Science&Engineering Ajou UniversitySuwon16499South Korea Department of Energy Systems Research Ajou UniversitySuwon16499South Korea

Perovskite oxides like S rTiO_(3) at the nanoscale are of interest for eme rging applications,including high-k dielectrics and ***,their synthesis requires long calcination at the elevated temperature,which is a barrier of their application to flexible ***,an effective laser-assisted sol-gel method to patternably produce S rTiO_(3) na noparticles(-100 nm)in selective areas on polyimide substrates(coated with ITO)is ***,the violet-laser power is just 1 W but sufficient to crystallize the material in a short period(a few seconds).Furthermore,developing a flexible device platform using carbon nanotubes(CNT)and SrTiO_(3) nanoparticles for detection of humidity changes at room temperature is *** sensor platform has both capacitive and resistive sensing *** resistive mode with a lower power usage(about 0.2μW)is suitable for long monitoring of humidity in the range of2%RH and *** capacitive mode with higher sensitivity,faster response/recovery time(1-3 min),and lower detection limit(0.5%RH)can be used for calibration *** performance of the flexible sensor is still maintained after 5000 bending cycles at 1.5-cm ***,our synthesis method and the flexible sensor show chances for mass-producing perovskite oxides at low cost for wearable electronics.

关键词： SrTiO_(3) Nanoparticles Laser annealing Patternable Flexible sensor CNT

Fluorine doping for improved thermoelectric properties of spark plasma sintered bismuth telluride

在线全文

学校读者我要写书评

暂无评论

Journal of Materials Science & Technology 2021年第31期90卷 225-235页

作者： Jinseo Kim le thai duy Hyunwoo Kang Byungmin Ahn Hyungtak Seo Department of Energy Systems Research Ajou UniversitySuwon 16499South Korea Department of Materials Science and Engineering Ajou UniversitySuwon 16499South Korea

Bismuth-telluride(Bi_(2)Te_(3))is considered to have the best thermoelectric properties at room temperature(ca.300 K).Thus,synthesis and improvement of Bi_(2)Te_(3)and its derivatives quickly and cost-effectively at favorite temperatures are of ***,doping fluorine(F)into electronic materials(e.g.,FTO)has gained attention;however,it is not applied to Bi_(2)Te_(3)till ***,our synthesis of F-doped Bi_(2)Te_(3)for thermoelectric application is introduced using spark plasma sintering(SPS)for Bi_(2)Te_(3)preparation and reactive ion etching(RIE,with SF_6 gas)for *** exposure time of SF_6 plasma is adjusted to evaluate F-doping effect on the thermoelectric properties of the *** characterizations,the increased electrical conductivity and the improved Seebeck coefficient of F-doped Bi_(2)Te_(3)are *** spectroscopic studies and DFT calculations,the main mechanism behind that improvement is *** also emphasizes the essential role of the F-doping(optimum treatment time of 40 s)in increasing the carrier concentrations as well as electrical *** increasing measurement temperature(300-510 K),F-doping raises the figure of merit of electron rich Bi_(2)Te_(3)from 1.0 to 1.11(peaked at 390 K).

关键词： Bismuth-telluride Fluorine Doping Thermoelectric Spark plasma sintering

Transparent, stretchable, and rapid-response humidity sensor for body-attachable wearable electronics

在线全文

学校读者我要写书评

暂无评论

Nano Research 2017年第6期10卷 2021-2033页

作者： Tran Quang Trung le thai duy Subramanian Ramasundaram Nae-Eung lee School of Advanced Materials Science & Engineering Sungkyunkwan University (SKKU) Suwon Kyunggi-do 16419 Republic of Korea Center for Water Resource Cycle Research Korea Institute of Science and Technology Hwarang-ro 14 gil Seongbuk-gu Seoul 02793 Republic of Korea SKKU Advanced Institute of Nanotechnology (SAINT) SKKU Suwon Kyunggi-do 16419 Republic of Korea Samsung Advanced Institute for Health Sciences & Technology (SAIHST) SKKU Suwon Kyunggi-do 16419 Republic of Korea

Stretchable and conformal humidity sensors that can be attached to the human body for continuously monitoring the humidity of the environment around the human body or the moisture level of the human skin can play an important role in electronic skin and personal healthcare applications. However, most stretchable humidity sensors are based on the geometric engineering of non-stretchable components and only a few detailed studies are available on stretchable humidity sensors under applied mechanical deformations. In this paper, we propose a transparent, stretchable humidity sensor with a simple fabrication process, having intrinsically stretchable components that provide high stretchability, sensitivity, and stability along with fast response and relaxation time. Composed of reduced graphene oxide-polyurethane composites and an elastomeric conductive electrode, this device exhibits impressive response and relaxation time as fast as 3.5 and 7 s, respectively. The responsivity and the response and relaxation time of the device in the presence of humidity remain almost unchanged under stretching up to a strain of 60% and after 10,000 stretching cycles at a 40% strain. Further, these stretchable humidity sensors can be easily and conformally attached to a finger for monitoring the humidity levels of the environment around the human body, wet objects, or human skin.

关键词： transparent stretchablehumidity sensor reduced graphene oxide wearable electronics body-attachable intrinsically stretchable components

Machine learning based models for predicting compressive strength of geopolymer concrete

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2024年第7期18卷 1028-1049页

作者： Quang-Huy le duy-Hung NGUYEN Thanh SANG-TO Samir KHATIR Hoang le-MINH Amir H.GANDOMI Thanh CUONG-le Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh 700000Vietnam Institute of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh 700000Vietnam Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly *** effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important *** facilitate engineering work on the material,an efficient predicting model is *** this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting *** standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is *** effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting *** three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and *** numerical results suggest that the DNN model generally outperforms the other two models.

关键词： geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines

Optimal Scheduling of Multiple Rail Cranes in Rail Stations with Interference Crane Areas

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Automation & Soft Computing 2024年第1期39卷 15-31页

作者： Nguyen Vu Anh duy Nguyen le thai Nguyen Huu Tho Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh City700000Vietnam

In this paper,we consider a multi-crane scheduling problem in rail stations because their operations directly influence the throughput of the rail *** particular,the job is not only assigned to cranes but also the job sequencing is implemented for each crane to minimize the makespan of cranes.A dual cycle of cranes is used to minimize the number of working cycles of *** rail crane scheduling problems in this study are based on the movement of *** consider not only the gantry moves,but also the trolley moves as well as the rehandle cases are also included.A mathematical model of multi-crane scheduling is *** traditional and parallel simulated annealing(SA)are adapted to determine the optimal scheduling *** examples are conducted to evaluate the applicability of the proposed *** of the proposed parallel SA is done by comparing it to existing previous *** of numerical computation highlighted that the parallel SA algorithm outperformed the SA and gave better solutions than other considered algorithms.

关键词： Multi-crane scheduling logistics containers makespan rail stations

Relationship between land use and user’s behavior along the street:A case study of Da Nang City,Viet Nam

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Architectural Research 2024年第1期13卷 144-163页

作者： duy Thinh Do duy Thao Do Faculty of Civil Engineering Ho Chi Minh City University of Technology and Education(HCMUTE)Ho Chi Minh City 71307Viet Nam School of Computing Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea

Human activities on street spaces are affected by both physical and non-physical settings on *** two aspects are primarily impacted by land use which results in the uneven distribution of different activities on *** research investigates land use patterns and their characteristics in association to user’s *** using mixed qualitative and quantitative research methods,e.g.,place-centered behavioral map,observation,visual encounter surveys,machine learning,the relationship between user’s behavior and land use characteristics along the street is *** user behaviors along both types of streets were classified into six main categories,with 26 *** observation results show that the residential use of land along the street was transformed into the commercial use for various types of shophouses based on the resident’s *** is a great correlation between land use and user’s *** findings give support to authorities to manage urban streets and develop a sustainable policy for improving street ***,this research contributes useful information to urban designers and planners in creating a successful street space that is appropriate for the Vietnam Community.

关键词： Land use pattern User’s behavior Da Nang Street space Data mining

Autohydrolysis treatment of bamboo and potassium oxalate(K_(2)C_(2)O_(4))activation of bamboo product for CO_(2) capture utilization

在线全文

学校读者我要写书评

暂无评论

Frontiers of Chemical Science and Engineering 2024年第4期18卷 63-72页

作者： Dang Duc Viet Doan Thi Thao Khuong duy Anh Toshiki Tsubota Department of Materials Science Faculty of EngineeringKyushu Institute of TechnologyFukuoka 804-8550Japan Vietnamese Academy of Forest Sciences Hanoi 10000Vietnam Collaborative Research Centre for Green Materials on Environmental Technology Kyushu Institute of TechnologyFukuoka 808-0196Japan

Typically,the hydroxide agents,such as sodium hydroxide and potassium hydroxide,which have corrosive properties,are used in the carbon activation *** this study,potassium oxalate(K_(2)C_(2)O_(4)),a less toxic and non-corrosive activating reagent,was used to synthesize activated carbon from the solid residue after autohydrolysis *** effect of the autohydrolysis treatment and the ratio of the K_(2)C_(2)O_(4)/solid residue are presented in this ***,the comparison between the activated carbon from bamboo and biochar from the solid residue are also *** resulting activated carbon from the solid residue exhibited a high surface area of up to 1432 m^(2)·g^(-1) and a total pore volume of up to 0.88 cm^(3)·g^(-1).The autohydrolysis treatment enhanced the microporosity properties compared to those without pretreatment of the activated *** microporosity of the activated carbon from the solid residue was dominated by the pore width at 0.7 nm,which is excellent for CO_(2) *** 25℃ and 1.013×10^(5) Pa,the CO_(2) captured reached up to 4.1 mmol·g^(-1).On the other hand,the ratio between K_(2)C_(2)O_(4) and the solid residue has not played a critical role in determining the porosity *** ratio of the K_(2)C_(2)O_(4)/solid residue of 2 could help the carbon material reach a highly microporous textural property that produces a high carbon capture *** finding proved the benefit of using the solid residue from the autohydrolysis treatment as a precursor material and offering a more friendly and sustainable activation carbon process.

关键词： activated carbon bamboo biochar CO_(2)capture potassium oxalate

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第3期78卷 3585-3602页

作者： duy Quang Tran Huy Q.Tran Minh Van Nguyen Faculty of Civil Engineering Nha Trang UniversityNha Trang650000Vietnam Robotics and Mechatronics Research Group Faculty of Engineering and TechnologyNguyen Tat Thanh UniversityHo Chi Minh City700000Vietnam Department of Civil Engineering Ho Chi Minh City University of Technology and EducationHo Chi Minh City700000Vietnam

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service *** volume is an influential parameter for planning and operating traffic *** study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning *** fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal ***,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition *** second aspect involves predicting traffic volume using the long short-term memory ***,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent ***,the fusion of the obtained results leads to a final traffic volume *** experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and *** achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the *** findings highlight the accuracy of traffic pattern ***,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

关键词： Ensemble empirical mode decomposition traffic volume prediction long short-term memory optimal hyperparameters deep learning

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data