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Velocity equation for grenades while impacting on dry sand media

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Defence Technology（防务技术） 2024年第5期35卷 168-173页

作者： Martin Macko xuan Son bui Kongsathit Phanthavong Duc Hung Pham Van Gion Do Van Minh Do Jiri Skala University of Defence BrnoCzech Republic Le Quy Don Technical University Ha NoiViet Nam

This paper deals with the collision of sphere shape grenades with sand media.The central issue of the article is the establishing of an empirical velocity equation of the grenade while impacting on sand that is used to solve motion equations of the mechanical mechanism inside the impact grenade fuze.The paper focuses on impact velocities that are lower than 5 m s^(-1).An experiment was conducted to study the velocity of the grenade while impacting on dry sand.A high-speed camera video was used to capture the grenade positions.The grenade velocity in the impact process was generated from these video data.Some types of fitting curves are used to regress the velocity equation of the grenade while interacting with the sand media and the best-fitting model is chosen.The result shows the regression curve has a high correlation with the experiment data for grenade velocities below 5 m s^(-1).The received regression equation is useful for analyzing the working ability of the inertial mechanism inside the impact grenade or analyzing and choosing the appropriate parameters of each part in the inertial mechanism to meet the required characteristics of the mechanism.

关键词： Dynamic response Impact velocity Impact grenade Low-velocity impact

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基于基因表达编程和粒子群优化鲁棒混合计算模型的深部地下矿井岩爆分类

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土木与环境工程学报（中英文） 2023年第2期45卷 21-38页

作者： TRAN Quang-Hieu bui xuan-nam NGUYEN Hoang Department of Surface Mining Mining Faculty/Innovations for Sustainable and Responsible Mining(ISRM)Research GroupHanoi University of Mining and GeologyHanoi 100000Vietnam

在深部地下采矿中,岩爆因具有许多不利影响(如对人员、设备、隧道/地下矿山工作面和开采周期等的影响)而被视为不确定性风险。由于其不确定性的特征,对岩爆趋势的准确预测和分类具有一定难度,且已有研究成果较少。提出一种基于基因表达... 详细信息

在深部地下采矿中,岩爆因具有许多不利影响(如对人员、设备、隧道/地下矿山工作面和开采周期等的影响)而被视为不确定性风险。由于其不确定性的特征,对岩爆趋势的准确预测和分类具有一定难度,且已有研究成果较少。提出一种基于基因表达编程(GEP)和粒子群优化(PSO)的鲁棒混合计算模型GEP-PSO,用于预测和分类深部开口的岩爆趋势,提高了预测和分类的准确性。在建立GEP-PSO模型的过程中,评估GEP模型中不同数量的基因(1~4)和连接功能(例如,加法、提取、乘法和除法)。收集246次岩爆发生的地质和施工因素,用于建立岩爆分类的GEP-PSO模型;应用处理数据集缺失值的技术改进数据集的属性;用相关矩阵选取潜在输入参数的特征;建立13个混合GEP-PSO模型,得到各模型的精度。结果表明:在GEP结构中具有3个基因和乘法连接函数的GEP-PSO模型具有最高的准确度(80.49%)。将获得的最佳GEP-PSO模型的结果与基于相同数据集开发的各种已有模型进行比较,结果表明,选择的GEP-PSO模型结果优于已有模型,表明提出的GEP-PSO模型在岩爆等级的预测和分类方面的准确性显著提高,可以应用于深开挖工程中,以准确预测和评估岩爆敏感性。

关键词：岩爆 GEP-PSO模型地下采矿深开挖风险评估

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A New Framework for Software Vulnerability Detection Based on an Advanced Computing

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Computers, Materials & Continua 2024年第6期79卷 3699-3723页

作者： bui Van Cong Cho Do xuan Department of Information Technology University of Economics and Technical IndustriesHanoi100000Vietnam Faculty of Information Security Posts and Telecommunications Institute of TechnologyHanoi100000Vietnam

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

关键词： Source code vulnerability source code vulnerability detection code property graph feature profile contrastive learning data rebalancing

Modeling Geometrically Nonlinear FG Plates: A Fast and Accurate Alternative to IGA Method Based on Deep Learning

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2793-2808页

作者： Se Li Tiantang Yu Tinh Quoc bui College of Mechanics and Materials Hohai UniversityNanjing211100China Duy Tan Research Institute for Computational Engineering(DTRICE) Duy Tan UniversityHo Chi Minh CityVietnam

Isogeometric analysis (IGA) is known to showadvanced features compared to traditional finite element approaches.Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functionalgrading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward adeep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complexIGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trainedusing the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationshipbetween the outputs and the inputs is constructed usingmachine learning so that the displacements can be directlyestimated by the deep learning network. To provide enough training data, we use S-FSDT Von-Karman IGA andobtain the displacement responses for different loads and gradient indexes. Results show that the recognition erroris low, and demonstrate the feasibility of deep learning technique as a fast and accurate alternative to IGA formodeling the geometrically nonlinear bending behavior of FG plates.

关键词： FG plates geometric nonlinearity deep learning BLSTM IGA S-FSDT

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

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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 loop.The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices.The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios.To 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 process.The approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical examples.The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data.Overall,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally intensive.This 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

Accuracy assessment of mine walls＇ surface models derived from terrestrial laser scanning

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International Journal of Coal Science & Technology 2018年第3期5卷 328-338页

作者： Nguyen Quoc Long Michal M. Buczek La Phu Hien Sylwia A. Szlapinska bui xuan nam Nguyen Viet Nghia Cao xuan Cuong Department of Mine Surveying Hanoi University of Mining and Geology Hanoi Vietnam Department of Mine Surveying AGH University of Science and Technology Kxakow Poland Department of Surveying and Spatial Data Analysis Hanoi University of Mining and Geology Hanoi Vietnam Department of Surface Mining Hanoi University of Mining and Geology Hanoi Vietnam

The monitoring of highwall slopes at open-pit mines is an important task to ensure safe mining. For this reason, several techniques such as total station, radar, terrestrial Light Detection and Ranging (LIDAR) can be employed for surface measurement. The objective of this study is to investigate mesh algorithms, which can be used to interpolate 3D models of pit walls. Experiments were carried out at Coc Sau open-pit mine at Quang Ninh province of Vietnam, and at experimental mine of Akademia Górniczo-Hutnicza University of Science and Technology in Cracow, Poland. First, 3D point cloud data for the study area was acquired by using terrestrial LIDAR, then was used to generate mesh surfaces using three algorithms—Delaunay 2.5D XY Plane, Delaunay 2.5D Best Fitting Plane, and Mesh from Points. After that, the results were rectified and optimized. Subsequently, the optimized meshes were used for generation of non-uniform rational basis spline (NURBS) surfaces. Then, the NURBS surface accuracy was assessed. The results showed that the average distance between surface and point cloud was within range of 5.6–5.8 mm with deviation of 6.2–6.8 mm, depending on the used mesh. Additionally, the quality of surfaces depends on the quality of input data set and the algorithm used to generate mesh network, and the accuracy of computed NURBS surfaces fitting into pointset was 4–5 times lower than that of optimized mesh fitting. However, the accuracy of the final product allows determining displacements on the level of centimeters.

关键词：表面模型精确性激光扫描陆上 Delaunay NURBS 评价激光雷达

Novel Soft ComputingModel for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 2227-2246页

作者： Quang-Hieu Tran Hoang Nguyen xuan-nam bui Department of Surface Mining Mining FacultyHanoi University of Mining and GeologyHanoi100000Vietnam Innovations for Sustainable and Responsible Mining(ISRM)Research Group Hanoi University of Mining and GeologyHanoi100000Vietnam

This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.

关键词： Mine blasting blast-induced ground vibration environmentally friendly blasting peak particle velocity bagging extra trees

基于基因表达编程和粒子群优化鲁棒混合计算模型的深部地下矿井岩爆分类（英文）

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土木与环境工程学报(中英文) 2022年

作者： Quang-Hieu TRAN xuan-nam bui Hoang NGUYEN Department of Surface Mining Mining Faculty Innovations for Sustainable and Responsible Mining (ISRM)Research Group Hanoi University of Mining and Geology

在深部地下采矿中，岩爆因具有许多不利影响（如，对人员、设备、隧道/地下矿山工作面和开采周期等的影响）而被视为不确定性风险。由于其不确定性的特征，对岩爆趋势的准确预测和分类具有一定难度，且已有研究成果较少。提出一种基于... 详细信息

在深部地下采矿中，岩爆因具有许多不利影响（如，对人员、设备、隧道/地下矿山工作面和开采周期等的影响）而被视为不确定性风险。由于其不确定性的特征，对岩爆趋势的准确预测和分类具有一定难度，且已有研究成果较少。提出一种基于基因表达编程（GEP）和粒子群优化（PSO）的鲁棒混合计算模型GEP-PSO，用于预测和分类深部开口的岩爆趋势，提高了预测和分类的准确性。在建立GEP-PSO模型过程中，评估了GEP模型中不同数量的基因（1～4）和连接功能（例如，加法、提取、乘法和除法）。收集了246次岩爆发生的地质和施工因素，用于建立岩爆分类的GEP-PSO模型；应用处理数据集缺失值的技术改进数据集的属性；用相关矩阵选取潜在输入参数的特征；建立了13个混合GEP-PSO模型，得到了各模型的精度。结果表明：在GEP结构中具有3个基因和乘法连接函数的GEP-PSO模型具有最高的准确度（80.49%）。将获得的最佳GEP-PSO模型的结果与基于相同数据集开发的各种已有模型进行比较，结果表明：选择的GEP-PSO模型结果优于已有模型，表明提出的GEP-PSO模型在岩爆等级的预测和分类方面的准确性显著提高，可以应用于深开挖工程中，以准确预测和评估岩爆敏感性。

关键词：岩爆 GEP-PSO模型地下采矿深开挖风险评估

Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1452-1465页

作者： Chengyu Xie Hoang Nguyen xuan-nam bui Van-Thieu Nguyen Jian Zhou School of Environment and Resources Xiangtan UniversityXiangtan411105China Mining Faculty Hanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyHanoi100000Viet Nam Artificial Intelligence Independent Research Group Hanoi100000Viet Nam School of Resources and Safety Engineering Central South UniversityChangsha410083China

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing rapidly.The coal mines have to pace up the mining operations with large production to meet the energy demand.This requirement has led underground coal mines to go deeper with more difficult conditions,especially the mining hazards,such as large deformations,rockburst,coal burst,roof collapse,to name a few.Therefore,this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method,using various novel intelligent techniques based on physics-based optimization algorithms(i.***.multi-verse optimizer(MVO),equilibrium optimizer(EO),simulated annealing(SA),and Henry gas solubility optimization(HGSO)) and adaptive neuro-fuzzy inference system(ANFIS),named as MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSOANFIS models.Accordingly,162 roof displacement events were investigated based on the characteristics of surrounding rocks,such as cohesion,Young’s modulus,density,shear strength,angle of internal friction,uniaxial compressive strength,quench durability index,rock mass rating,and tensile strength.The MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground mines.The results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%.Remarkably,the SA-ANFIS model yielded the most dominant accuracy(i.e.92%).Based on the accurate predictions from the proposed techniques,the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal,especially in the underground coal mines exploited by the longwall mining.

关键词： Roof displacement Longwall mining Underground mine Physics-based optimization Risk assessment Mining hazards

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