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Classification and Prediction of Communication Cables Length Based on S-Parameters Using a machine-learning method

IEEE ACCESS 2023年 11卷 108041-108049页

作者： Al Bataineh, Mohammad Umar, Malik Mohamed Moin, Aquib Hussein, Mousa I. Al Ahmad, Mahmoud United Arab Emirates Univ Elect & Commun Engn Dept Al Ain U Arab Emirates Yarmouk Univ Telecommun Engn Dept Irbid 21163 Jordan United Arab Emirates Univ Phys Dept Al Ain U Arab Emirates

The length of the communication cables is a significant indicator of signal integrity. The associated scattering parameters characteristics of a communication cable effectively enable its length estimation. This paper proposes a novel machine learning-based algorithm that utilizes Support Vector machines (SVM) to predict and classify cable lengths. Specifically, the algorithm employs an SVM Regression Model (SVR) and an SVM Classification Model (SVC) to predict and classify cable lengths based on their S-parameters (S-21 measurements). As the data under investigation are inseparable, linear, and high-dimensional, SVM has been implemented. The current approach was implemented to verify the length of two datasets, underground and overground cables, with different environmental conditions. The present research introduces an innovative machine-learning algorithm that employs an S-parameter-centric methodology to predict variations in communication cable lengths. Specifically, the SVR model achieved R-2 values of approximately 0.987 for underground cables and 0.991 for overground cables. Meanwhile, the SVC model demonstrated varying levels of accuracy, with optimal performance seen in five classes for underground cables and four classes for overground cables. The SVM model efficiently extracts and weighs features for high-accuracy predictions in nonlinear, multiclass scenarios, making it the optimal model for this work.

关键词： Power cables Support vector machines Scattering parameters Communication cables Predictive models machine learning Static VAr compensators Classification algorithms Classification prediction cable length S-parameters machine-learning method

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Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review

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JOURNAL OF ENGINEERING AND TECHNOLOGICAL SCIENCES 2023年第4期55卷 466-477页

作者： Vung, Do Van Tran, The Viet Ha, Nguyen Duc Duong, Nguyen Huy Thuyloi Univ Dept Civil Engn 175 Tay Son Hanoi Vietnam Vietnam Inst Geosci & Mineral Resources 67 Chien Thang Hanoi Vietnam

Rainfall-induced landslides threaten lives and properties globally. To address this, researchers have developed various methods and models that forecast the likelihood and behavior of rainfall-induced landslides. These methodologies and models can be broadly classified into three categories: empirical, physical-based, and machine-learning approaches. However, these methods have limitations in terms of data availability, accuracy, and applicability. This paper reviews the current state-of-the-art of rainfall-induced landslide prediction methods, focusing on the methods, models, and challenges involved. The novelty of this study lies in its comprehensive analysis of existing prediction techniques and the identification of their limitations. By synthesizing a vast body of research, it highlights emerging trends and advancements, providing a holistic perspective on the subject matter. The analysis points out that future research opportunities lie in interdisciplinary collaborations, advanced data integration, remote sensing, climate change impact analysis, numerical modeling, real-time monitoring, and machine learning improvements. In conclusion, the prediction of rainfall-induced landslides is a complex and multifaceted challenge, and no single approach is universally superior. Integrating different methods and leveraging emerging technologies offer the best way forward for improving accuracy and reliability in landslide prediction, ultimately enhancing our ability to manage and mitigate this geohazard.

关键词： climate change empirical method landslide monitoring machine-learning method physical-based method rainfall-induced landslides

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SH3GL2 and MMP17 as lung adenocarcinoma biomarkers: a machine-learning based approach

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BIOCHEMISTRY AND BIOPHYSICS REPORTS 2024年 38卷 101693页

作者： Tian, Zengjian Yu, Shilong Cai, Ruizhi Zhang, Yinghui Liu, Qilun Zhu, Yongzhao Ningxia Med Univ Gen Hosp Yinchuan 750004 Ningxia Peoples R China Ningxia Med Univ Inst Med Sci Gen Hosp Yinchuan 750004 Ningxia Peoples R China

Objective: Using bioinformatics machine learning methods, our research aims to identify the potential key genes associated with Lung adenocarcinoma (LUAD). methods: We obtained two gene expression profiling microarrays (GSE68571 and GSE74706) from the public Gene Expression Omnibus (GEO) database at the National Centre for Biotechnology Information (NCBI). The purpose was to identify Differentially Expressed Genes (DEGs) between the lung adenocarcinoma group and the healthy control group. The limma R package in R was utilized for this analysis. For the differential gene diagnosis of lung adenocarcinoma, we employed the least absolute shrinkage and selection operator (LASSO) regression and SVM-RFE screening crossover. To evaluate the performance, ROC curves were plotted. We performed immuno-infiltration analysis using CIBERSORT. Finally, we validated the key genes through qRT-PCR and Western-blot verification, then downregulated MMP17 gene expression, upregulated SH3GL2 gene expression, and performed CCK8 experiments. Results: A total of 32 Differentially Expressed Genes (DEGs) were identified. Two diagnostic marker genes, SH3GL2 and MMP17, were selected by employing LASSO and SVM-RFE machine learning methods. In Lung adenocarcinoma cells, the expression of MMP17 was observed to be elevated compared to normal lung epithelial cells in the control group (P < 0.05). In contrast, a down-regulation of SH3GL2 was found in Lung adenocarcinoma cells (P < 0.05). Finally, we downregulated MMP17 and upregulated SH3GL2 gene expression, then the CCK8 showed that the proliferation of both lung cancer cells was inhibited. Conclusion: SH3GL2 and MMP17 are expected to be potential biomarkers for Lung adenocarcinoma.

关键词： Lung adenocarcinoma Bioinformatics machine-learning method Biomarkers Genes

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Predicting Diabetes in Patients with Metabolic Syndrome Using machine-learning Model Based on Multiple Years? Data

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DIABETES METABOLIC SYNDROME AND OBESITY-TARGET & THERAPY 2022年 15卷 2951-2961页

作者： Li, Jing Xu, Zheng Xu, Tengda Lin, Songbai Peking Union Med Coll Hosp Dept Hlth Management Beijing Peoples R China Digital Hlth China Technol Co Ltd Dept AI Res Beijing Peoples R China Peking Union Med Coll Hosp Dept Hlth Management 1 Shuaifuyuan Beijing 100730 Peoples R China

Purpose: To evaluate the performance of machine-learning models based on multiple years of continuous data to predict incident diabetes among patients with metabolic *** and methods: The dataset comprises the health records from 2008 to 2020 including 4510 nondiabetic participants with metabolic syndrome (MetS) at baseline and with at least 6 years of records. MetS was defined according to the International Diabetes Federation (IDF) criteria. Overall, 332 patients developed incident diabetes during the 7 +/- 1.4 years of follow-up. Three popular classification algorithms were evaluated on the dataset: logistic regression, random forest, and Xgboost. Five models including singleyear models (year 1, year 2, and year 3) and multiple-year models (year 1-2 and year 1-3) were developed for each algorithm. Results: The model performances improved with the increasing longitudinal dataset as the area under the receiver operating characteristic curve (AUROC) was boosted for both random forest (year 1-3: AUROC=0.893;year 3: AUROC=0.862;year 1-2: AUROC=0.847;year 2: AUROC=0.838) and Xgboost (year 1-3: AUROC=0.897;year 3: AUROC=0.833;year 1-2: AUROC=0.856;year 2: AUROC=0.823) model. In the multiple-year models, the highest fasting plasma glucose, followed by the mean or lowest level of HbA1c and BMI had the most important predictive value for the onset of diabetes. In the "1-3" year model, "delta weight" which reflects the fluctuations of yearly change of weight was the fourth-most important ***: This study demonstrated improved performance with the accumulation of longitudinal data when using machine learning for diabetes prediction in MetS patients. For individuals with similar clinical parameters, the variation trends of these parameters could change the risk of future diabetes. This result indicated that models based on longitudinal multiple years' data may provide more personalized assessment tools for risk evaluation.

关键词： diabetes metabolic syndrome machine-learning method prevention

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Explainable machine-learning Model for Rapid Damage Assessment of CFST Columns after Close-In Explosion

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JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES 2024年第3期38卷

作者： Li, Jie Pang, Yanfen Wang, Kansheng Zhang, Xuejie Wang, Ning Tianjin Chengjian Univ Tianjin Key Lab Civil Struct Protect & Reinforceme Tianjin 300384 Peoples R China Tianjin Chengjian Univ Sch Civil Engn Tianjin 300384 Peoples R China Hebei Univ Water Resources & Elect Engn Cangzhou 061001 Peoples R China Wenzhou Univ Coll Civil Engn & Architecture Tianjin Peoples R China

In the present study, the dynamic response and damage of concrete-filled steel tubular (CFST) columns under close-in explosion were numerically studied. An extensive parametric study was carried out to investigate the effects of column height, diameter, wall thickness, yield strength of steel, compressive strength of concrete, and axial load ratio on the residual midheight displacement (RMHD) and residual axial load-bearing capacity (RALBC). It was found that the RALBC is strongly correlated with the RMHD under different explosion scenarios. Three models were developed using Extreme Gradient Boosting (XGBoost) based on a database comprising 1,708 circular CFST column samples. These models aimed to predict the relationship between RMHD and RALBC, utilizing different combinations of input variables. Accurate prediction results can be obtained from all the models, and the selection of a model can be based on the availability of known input variables. The third prediction model, which does not require knowledge of the blast loading parameters and axial load ratio, which are usually difficult to obtain, can yield accurate results. Therefore, it can be used to quickly evaluate the RALBC of CFST columns. Finally, the prediction model was further interpreted locally and globally using the additive feature attribution method Shapley Additive Explanation (SHAP). Through the SHAP interpretation, the contribution of each input variable to the RALBC of CFST columns was analyzed. This provided valuable insights into the impact of individual variables on the prediction results.

关键词： Concrete-filled steel tubular (CFST) column Blast loading Damage assessment machine-learning method Extreme Gradient Boosting (XGBoost) Shapley Additive Explanation (SHAP) method

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Unraveling the atomic structure of the R(15x12) reconstruction of carburized W(110) based on ab initio calculations

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APPLIED SURFACE SCIENCE 2024年 643卷

作者： Wang, Ye Moradpur-Tari, Ehsan Zadin, Veronika Kyritsakis, Andreas Univ Tartu Inst Technol Nooruse 1 EE-50411 Tartu Estonia

Carburized W(1 1 0) surfaces exhibit a nm-scale rectangular periodicity on their surface, thus providing an excellent platform for the assembly of self-organized nanostructures, i.e., nano-patterning. Understanding their atomic structure and surface properties is essential for their further exploitation in this direction. Two types of such surface reconstructions have been observed, namely the R(15 x 3) and the R(15 x 12). Although the atomic structure of the R(15 x 3) surface has been recently discovered, the one of the R(15 x 12) remains unknown. In this work, we propose a new atomic structure model for the R(15 x 12) surface. We then employ density functional theory and machine learning based molecular dynamics calculations to show that the proposed model is consistent with the experimental scanning tunneling microscopy imaging of the surfaces.

关键词： Density functional theory Carburized tungsten Nano-patterning templates Atomic surface structure Scanning tunnelling microscopy machine-learning method

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Comparison of machine-learning methods for Urban Land-Use Mapping in Hangzhou City, China

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REMOTE SENSING 2020年第17期12卷 2817-2817页

作者： Mao, Wanliu Lu, Debin Hou, Li Liu, Xue Yue, Wenze Zhejiang Univ Dept Land Management Hangzhou 310058 Peoples R China Zhejiang Acad Surveying & Mapping Hangzhou 311100 Peoples R China

Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickl

关键词： urban land use machine-learning method multi-source data Hangzhou

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Motor-cognitive functions required for driving in post-stroke individuals identified via machine-learning analysis

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2023年第1期20卷 139-139页

作者： Tabuchi, Genta Furui, Akira Hama, Seiji Yanagawa, Akiko Shimonaga, Koji Xu, Ziqiang Soh, Zu Hirano, Harutoyo Tsuji, Toshio Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Hiroshima 7398527 Japan Hiroshima Univ Grad Sch Adv Sci & Engn 1-4-1 Kagamiyama Hiroshima 7398527 Japan Hiroshima Univ Dept Neurosurg Grad Sch Biomed & Hlth Sci 1-2-3 KasumiMinami Ku Hiroshima Hiroshima 7348551 Japan Hibino Hosp Dept Rehabil 7-9-2 Tomo-HigashiAsaminami Ku Hiroshima 7313164 Japan Asa Citizens Hosp Hiroshima City North Med Ctr Dept Neurosurg & Intervent Neuroradiol 1-2-1 Kameyama Minami Asakitaku Hiroshima 7310293 Japan Fujita Hlth Univ Sch Med Sci Dept Med Equipment Engn Clin Collaborat Unit 1-98 Dengakugakubo Toyoake Aichi 4701192 Japan

Background People who were previously hospitalised with stroke may have difficulty operating a motor vehicle, and their driving aptitude needs to be evaluated to prevent traffic accidents in today's car-based society. Although the association between motor-cognitive functions and driving aptitude has been extensively studied, motor-cognitive functions required for driving have not been *** In this paper, we propose a machine-learning algorithm that introduces sparse regularization to automatically select driving aptitude-related indices from 65 input indices obtained from 10 tests of motor-cognitive function conducted on 55 participants with stroke. Indices related to driving aptitude and their required tests can be identified based on the output probability of the presence or absence of driving aptitude to provide evidence for identifying subjects who must undergo the on-road driving test. We also analyzed the importance of the indices of motor-cognitive function tests in evaluating driving aptitude to further clarify the relationship between motor-cognitive function and driving *** The experimental results showed that the proposed method achieved predictive evaluation of the presence or absence of driving aptitude with high accuracy (area under curve 0.946) and identified a group of indices of motor-cognitive function tests that are strongly related to driving *** The proposed method is able to effectively and accurately unravel driving-related motor-cognitive functions from a panoply of test results, allowing for autonomous evaluation of driving aptitude in post-stroke individuals. This has the potential to reduce the number of screening tests required and the corresponding clinical workload, further improving personal and public safety and the quality of life of individuals with stroke.

关键词： Driving aptitude machine-learning method Motor-cognitive functions Post-stroke individuals

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Deep learning method for predicting the mechanical properties of aluminum alloys with small data sets

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MATERIALS TODAY COMMUNICATIONS 2021年 28卷

作者： Yu, Zhang Ye, Sang Sun, Yanli Zhao, Hucheng Feng, Xi-Qiao Tsinghua Univ Inst Biomech & Med Engn Dept Engn Mech AML Beijing 100084 Peoples R China

Big data is usually needed for a deep learning method to predict the properties of materials, but, in practice, only limited data sets are available for engineering materials. In this study, we develop a deep neural network (DNN) to predict the mechanical properties of aluminum alloys from small data sets of chemical compositions and processing parameters. Through pre-training the DNN model to initialize a better set of parameters and tuning the model parameters, the present method can efficiently provide an accurate mapping between the mechanical properties and the composition and processing parameters of aluminum alloys. Compared with the support vector regression (SVR) and the shallow neural network (SNN), the present DNN method exhibits higher prediction accuracy and better generalization performance. It can be easily extended to other kinds of materials, and this work highlights the potential of the DNN method in data-driven material design. Data Availability: All original data and code that support the findings of this study are available from the corresponding authors upon request.

关键词： Deep neural network machine-learning method Aluminum alloy Small data set Mechanical properties

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Deep learning method for determining the surface elastic moduli of microstructured solids

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EXTREME MECHANICS LETTERS 2021年 44卷

作者： Ye, Sang Huang, Wei-Zhi Li, Min Feng, Xi-Qiao Tsinghua Univ Inst Biomech & Med Engn AML Beijing 100084 Peoples R China Tsinghua Univ Dept Engn Mech SKLT Beijing 100084 Peoples R China

Surface microstructures greatly affect many physical and mechanical properties of solid materials. Surface constitutive relations are often required to analyze the mechanical responses of materials and structures at the micro scales, but it remains a challenge to determine the surface constitutive parameters of microstructured materials. In this paper, a machine learning-based approach is proposed to predict the surface elastic properties of materials with complex microstructures on their surfaces. Using a data set generated from the finite element method, we demonstrate that the trained deep neural network can efficiently provide an accurate mapping between the equivalent surface elastic properties (e.g., elastic moduli and Poisson's ratios) and the mechanical and geometric features of surface microstructures. This study provides an efficient way to evaluate the surface properties of materials and promises for wide applications in the analysis of micro-/nanostructured materials and devices. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Microstructure Surface effect Surface elasticity Deep neural network machine-learning method

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