检索结果-内蒙古大学图书馆

Classification models for impact damage of fiber reinforced concrete panels using tree-based learning algorithms

STRUCTURES 2023年 53卷 119-131页

作者： Thai, Duc-Kien Le, Dai-Nhan Doan, Quoc Hoan Pham, Thai-Hoan Nguyen, Dang-Nguyen Sejong Univ Dept Civil & Environm Engn 98 Gunja Dong Seoul 143747 South Korea Hanoi Univ Civil Engn Dept Concrete Struct 55 Giai Phong Hanoi Vietnam Ho Chi Minh City Open Univ Fac Civil Engn Dept Struct Engn Adv Struct Engn Lab Ho Chi Minh City Vietnam

This study aims to develop the machine learning models for classification of the local damage levels of FRC panels subjected to missile impact load using the tree-based algorithms and ensembles. Six different algorithms, including Decision tree, Random Forest, Bagging, AdaBoost, XGBoost, and CatBoost were trained and evaluated based on a dataset collected from 176 experiments of FRC panels under missile impact, which consists of 15 input parameters of geometries, materials, and boundary conditions and one output parameter of local damage level of FRC panels. The Bayesian Optimization algorithm and k-fold cross validation were also utilized to achieve higher accuracy in prediction ability of the models. The obtained results showed that the proposed models can predict the local damage of FRC panels subjected to the missile impact load with acceptable accuracy. The models using ensemble methods have better performance than single estimator model in prediction and each model using ensemble method has its own strength and is suitable for different criteria when classifying. For imbalanced dataset, Random Forest can be chosen as the most suitable classification model for the dataset in this study.

关键词： tree-based algorithm Bayesian optimization Fiber reinforced concrete Impact load Machine learning

来源：评论

学校读者我要写书评

暂无评论

Peak-based Machine Learning for Plastic Type Classification in Time-of-Flight Secondary Ion Mass Spectrometry

引用

JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024年第12期35卷 3107-3115页

作者： Son, Jin Gyeong Shon, Hyun Kyong Kim, Ji-Eun Lee, In-Ho Lee, Tae Geol Korea Res Inst Stand & Sci Nanobio Measurement Grp Daejeon 34113 South Korea AIRISS Co Ltd Res Inst Daejeon 34037 South Korea Korea Res Inst Stand & Sci Mat Property Metrol Grp Daejeon 34113 South Korea

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) measurement data and machine learning were used in this work to classify six different types of plastics. In order to take into account the characteristics of the measurement data, the local maxima of the measurement data were first examined in a preprocessing step. Several machine learning methods were then implemented to create a model that could successfully classify the plastics. To visualize the data distribution, we applied a dimensionality reduction method, namely, principal component analysis. Finally, to distinguish between the six types of plastics, we conducted an ensemble analysis using four tree-based algorithms: decision tree, random forest, gradient boosting, and LIGHTGBM. This approach can identify the feature importance of plastic samples and allow the inference of the chemical properties of each plastic type. In this way, ToF-SIMS data could be utilized to successfully classify plastics and enhance explainability.

关键词： machine learning mass spectra preprocessing ToF-SIMS PCA tree-based algorithm featureimportance

来源：评论

学校读者我要写书评

暂无评论

Machine translation model for effective translation of Hindi poetries into English

引用

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2022年第1期34卷 95-109页

作者： Chakrawarti, Rajesh Kumar Bansal, Jayshri Bansal, Pratosh Devi Ahilya Vishwavidyalaya Inst Engn & Technol Fac Engn Comp Engn Indore 452001 Madhya Pradesh India Devi Ahilya Vishwavidyalaya Human Resource Dev Ctr Indore 452001 Madhya Pradesh India Devi Ahilya Vishwavidyalaya Inst Engn & Technol Dept Informat Technol Indore 452001 Madhya Pradesh India

The Word Sense Disambiguation (WSD) is a process of disambiguating the sense of the text according to its context. Machine translation is one of the challenging task since it requires effective representation of the text to capture semantic relation between Hindi lyrics in English normal language behaviour. This paper focuses on WSD methods to deal with dialects that convert Hindi lyrics to English in its syntactic structure of the words. WSD is a phenomenon for disambiguating the text so that machine would be capable to deduce correct sense of individual given words. WSD is critical for solving natural language tasks such as Machine Translation (MT) and speech processing. The distinguishing proof of significant words in Hindi as the language is not as simple as that of dialects in English. The interpretations of sonnets through the machines are exceptionally essential and deliberate about mind-blowing events. The interpretation of English ballads into other local dialects can turn out to be very straightforward, however, vice-versa is troublesome. This is due to the assortment of structures, classes, and feelings of the local dialects. Various endeavours have been connected far and wide towards the programmed interpretation of ballads from local dialects into English. In this paper, we propose a half breed MT (HBMT) procedure driven by the standard based MT together with measurements based on statistical machine translation (SMT) and rule-based machine translation (RBMT) for WSD in natural script Hindi in English Lyrics. This proposed method improves the semantic and syntactic accuracy of a machine interpretation framework. Finally, the proposed approach result is compared with the machine translation methods such a Google and Microsoft Bing Babylonian and HMT translators provided achieves a better outcome compared to the existing standards.

关键词： Word sense disambiguation machine translation rule-based method tree-based algorithm statistical-based method

来源：评论

学校读者我要写书评

暂无评论

Applying Machine Learning algorithms to Predict Potential Energies and Atomic Forces during C-H Activation

引用

JOURNAL OF THE KOREAN PHYSICAL SOCIETY 2020年第8期77卷 680-688页

作者： Lee, Seunghee Kim, Hyun Woo Im, Jino Kim, Seok Ki Kim, Yong Tae Chang, Hyunju Ko, Tae-Wook Lee, Jonggul Hyon, YunKyong Konyang Univ Hosp KYUH Daejeon 35365 South Korea Natl Inst Math Sci NIMS Daejeon 34047 South Korea Korea Res Inst Chem Technol KRICT Daejeon 34114 South Korea

Molecular dynamics (MD) simulations are useful in understanding the interaction between solid materials and molecules. However, performing MD simulations is possible only when interatomic potentials are available and constructing such interatomic potentials usually requires additional computational work. Recently, generating interatomic potentials was shown to be much easier when machine learning (ML) algorithms were used. In addition, ML algorithms require new descriptors for improved performance. Here, we present an ML approach with several categories of atomic descriptors to predict the parameters necessary for MD simulations, such as the potential energies and the atomic forces. We propose several atomic descriptors based on structural information and find that better descriptors can be generated from eXtreme gradient boosting (XGBoost). Moreover, we observe fewer descriptors that perform better in predicting the potential energies and the forces during methane activation processes on a catalytic Pt(111) surface. These results were consistently observed in two different ML algorithms: fully-connected neural network (FNN) and XGBoost. Taking into account the advantages of FNN and XGBoost, we propose an efficient ML model for estimating potential energies. Our findings will be helpful in developing new ML potentials for long-time MD simulations.

关键词： Machine-learning potential tree-based algorithm Feature-engineering

来源：评论

学校读者我要写书评

暂无评论

Advanced machine learning approaches for predicting permeability in reservoir pay zones based on core analyses

引用

HELIYON 2024年第12期10卷 e32666页

作者： Hussen, Amad Munshi, Tanveer Alam Jahan, Labiba Nusrat Hashan, Mahamudul Shahjalal Univ Sci & Technol Dept Petr & Min Engn Sylhet 3114 Bangladesh

Permeability is the most important petrophysical characteristic for determining how fluids pass through reservoir rocks. This study aims to develop and assess intelligent computer-based models for predicting permeability. The research focuses on three novel models -Decision tree, Bagging tree, and Extra trees -while also investigating previously applied techniques such as random forest, support vector regressor (SVR), and multiple variable regression (MVR). The primary dataset consists of 197 data points from a heterogeneous petroleum reservoir in the Jeanne d 'Arc Basin, including laboratory-derived permeability ( K ), oil saturation ( S O ), water saturation ( S W ), grain density ( rho gr ), porosity ( phi), and depth. The most effective machine learning models are identified by a thorough analysis that makes use of a variety of statistical metrics, such as the coefficient of the determinant (R 2 ), mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), maximum error (maxE), and minimum error (minE). Additionally, core features are ranked based on their importance in permeability modeling. This study deviates from conventional approaches by proposing an efficient means of forecasting permeability, reducing reliance on labor-intensive and time-consuming laboratory work. The findings reveal that MVR is unsuitable for permeability prediction, with all developed models outperforming it. Extra trees emerges as the most accurate model, with an R 2 of 0.976, while random forest and bagging tree exhibit slightly lower R 2 values of 0.961 and 0.964, respectively. The ranking of these algorithms based on performance criteria is as follows: extra trees, bagging tree, random forest, SVR, decision tree, and MVR. The study also presents a detailed analysis of the impact of input parameters, highlighting porosity ( phi) and water saturation ( S W ) as the most influential, while grain density ( rho gr ), oil sa

关键词： Reservoir characterization Machine learning tree-based algorithm Core data Permeability modeling Porosity Oil saturation Core features ranking

来源：评论

学校读者我要写书评

暂无评论

Content-based Visual Summarization for Image Collections

引用

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021年第4期27卷 2298-2312页

作者： Pan, Xingjia Tang, Fan Dong, Weiming Ma, Chongyang Meng, Yiping Huang, Feiyue Lee, Tong-Yee Xu, Changsheng Chinese Acad Sci Inst Automat NLPR Beijing 100864 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Fosafer Beijing 100142 Peoples R China Kuaishou Technol Beijing 100085 Peoples R China Didi Chuxing Beijing 100000 Peoples R China Tencent YouTu Lab Shanghai 200233 Peoples R China Natl Cheng Kung Univ 34912 Tainan 701 Taiwan

With the surge of images in the information era, people demand an effective and accurate way to access meaningful visual information. Accordingly, effective and accurate communication of information has become indispensable. In this article, we propose a content-based approach that automatically generates a clear and informative visual summarization based on design principles and cognitive psychology to represent image collections. We first introduce a novel method to make representative and nonredundant summarizations of image collections, thereby ensuring data cleanliness and emphasizing important information. Then, we propose a tree-based algorithm with a two-step optimization strategy to generate the final layout that operates as follows: (1) an initial layout is created by constructing a tree randomly based on the grouping results of the input image set;(2) the layout is refined through a coarse adjustment in a greedy manner, followed by gradient back propagation drawing on the training procedure of neural networks. We demonstrate the usefulness and effectiveness of our method via extensive experimental results and user studies. Our visual summarization algorithm can precisely and efficiently capture the main content of image collections better than alternative methods or commercial tools.

关键词： Visualization Layout Correlation Backpropagation Optimization Computational modeling Semantics Visual summarization photo collection collage layout tree-based algorithm gradient back propagation

来源：评论

学校读者我要写书评

暂无评论

Mining Valuable Fuzzy Patterns via the RFM Model 22

Mining Valuable Fuzzy Patterns via the RFM Model

引用

22nd IEEE International Conference on Data Mining (ICDM)

作者： Qi, Yanlin Lai, Fuyin Chen, Guoting Gan, Wensheng Harbin Inst Technol Shenzhen Sch Sci Shenzhen Peoples R China Jinan Univ Coll Cyber Secur Guangzhou Peoples R China

ISBN: (纸本)9798350346091

This paper aims to propose an effective algorithm to discover valuable patterns by applying the fuzzy method to the RFM model. RFM analysis is a common method in customer relationship management, through which we can identify valuable customer groups. By combining RFM analysis with frequent pattern mining, valuable RFM-patterns can be found from the RFM-pattern-tree, such as the RFMP-growth algorithm. Aiming to mine patterns that have quantitative relationships among items, we introduce the fuzzy method in the RFM model, and we present a fuzzy-RFU-tree algorithm in which a new pruning strategy is proposed to prune candidate patterns. Experiments show the effectiveness of the new algorithm. The new algorithm guarantees a high overlap degree with the RFM-patterns generated by RFMP-growth, with more valuable information (with additional fuzzy level) in the mined patterns.

关键词： Pattern mining RFM model Fuzzification tree-based algorithm

来源：评论

学校读者我要写书评

暂无评论

Fall Risk Prediction for Elderly Using Head-Mounted Inertial Sensors and tree-based Models

Fall Risk Prediction for Elderly Using Head-Mounted Inertial...

引用

2024 IEEE Sensors Conference

作者： Chen, Yu-Zheng Lin, Fang-Yi Tseng, Li-Fan Chen, Chien-Hsu Sung, Pi-Shan Lin, Chih-Lung Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan Natl Cheng Kung Univ Dept Ind Design Tainan 70101 Taiwan Natl Cheng Kung Univ Inst Clin Med Tainan 70101 Taiwan

ISBN: (纸本)9798350363524;9798350363517

In recent years, with the increasing number of elderly people, falls have caused significant concerns and troubles in their daily lives. Therefore, the importance of fall prediction is indispensable. This work applies a head-mounted device with an inertial measurement unit, and tree-based models are used to predict the future fall risk of elderly individuals. Elderly participants were asked to stand in place with their eyes closed for 1 minute. A random forest and Morse fall scale were utilized to select features of acceleration and angular velocity from time-domain and frequency-domain. Because the decision tree is more lightweight than the random forest, the decision tree can be more suitable for use on edge devices. Therefore, a decision tree was utilized to classify the future fall risk of elderly individuals. With the optimal feature subsets, the 10-fold cross-validation results yield recall, precision, and F1-score of 0.818, 0.692, and 0.750, respectively.

关键词： head-mounted device inertial measurement unit fall risk prediction tree-based algorithm

来源：评论

学校读者我要写书评

暂无评论

algorithmic Solutions for RFID Tag Anti-Collision Problem in Supply Chain Management 10

Algorithmic Solutions for RFID Tag Anti-Collision Problem in...

引用

10th International Conference on Ambient Systems, Networks and Technologies (ANT) / 2nd International Conference on Emerging Data and Industry 4.0 (EDI40)

作者： Pal, Kamalendu City Univ London Dept Comp Sci London ECV 0HB England

The Radio Frequency IDentification (RFID) is a wireless communication technology that enables automatic identification, tracking and data collection from any tagged object in a supply chain operating environment. A simple RFID system uses radio signals to transmit data via a tiny portable device, called a tag, which is read by an RFID reader and processed by the corporate information system (IS) to meet the needs of business management. One of the important performance issues in this system is to resolve RFID tag collision. Tag collision happens when two or more tags reflects-back their individual identification radio signals to the reader at the same time thus confusing the reader identification process. Different algorithmic solutions on tag collision are available. They are generally in two main categories of anti-collision problems: ALOHA-based solutions and treebased solutions. However, ALOHA-based algorithms suffer from a time-related starvation problem. The well-known tree-based algorithms are Binary tree (BT) and Query tree (QT). In addition, QT algorithms are very efficient in memory utilization in comparison to other algorithmic solutions. This paper presents simulation-based experimental results on the performance of some well-known BT-based algorithms: simple Binary Search algorithm (BSA), Dynamic Binary Search algorithm (DBSA), and Backtrack Binary algorithm (BBA). (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Radio Frequency Identification Wireless Communication Supply Chain Management Tag Collision ALOHA-based algorithm tree-based algorithm

来源：评论

学校读者我要写书评

暂无评论

Ramification algorithm for graphene sample-defect localization 25

Ramification algorithm for graphene sample-defect localizati...

引用

Conference on Automatic Target Recognition XXV

作者： Sokolnikov, Andre Visual Solut & Applicat South Bend IN 46615 USA

ISBN: (纸本)9781628415926

Recent development of a new 2D material graphene necessitates sample characterization (in particular localization and distribution of defects). The presence of defects is unavoidable, however, it is possible to determine and predict defect distribution in graphene samples prior to the actual device making. A ramification algorithm is used for the above purpose.

关键词： graphene adaptive modeling prediction modeling tree-based algorithm 2D systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：