检索结果-内蒙古大学图书馆

Remote Sensing Estimation of Forest Aboveground Biomass Based on lasso-SVR

FORESTS 2022年第10期13卷 1597页

作者： Wang, Ping Tan, Sanqing Zhang, Gui Wang, Shuang Wu, Xin Cent South Univ Forestry & Technol Sch Forestry Changsha 410004 Peoples R China Key Lab Digital Dongting Lake Hunan Prov Changsha 410004 Peoples R China Natl Forest Fire Prevent Virtual Simulat Expt Tea Changsha 410004 Peoples R China

With the Lutou Forest Farm as the research area, the lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) model. The most suitable SVR model was selected to estimate the aboveground biomass of the forest through the comparison of the kernel function and optimal parameters, and the spatial distribution map of the aboveground biomass in the study area was drawn. The significance analysis of special variables showed good correlations between forest aboveground biomass and each vegetation index. There was a more significant correlation with some remote sensing bands, a less significant correlation with some texture features, and a strong correlation with DEM in the terrain features. When the parameters C is 2 and g is 0.01, the SVR model has the highest precision, which can illustrate 73% of the forest aboveground biomass, with the validation set R-2 being 0.62. The statistical analysis of the results shows that the total aboveground biomass of the Lutou Forest Farm is 4.82x10(5) t. The combination of lasso with the SVR model can improve the estimation accuracy of forest aboveground biomass, and the model has a strong generalization ability.

关键词： aboveground biomass remote sensing estimation lasso algorithm support vector regression model

来源：评论

学校读者我要写书评

暂无评论

Prognostic significance of a 3-gene ferroptosis-related signature in lung cancer via lasso analysis and cellular functions of UBE2Z

引用

COMPUTATIONAL BIOLOGY AND CHEMISTRY 2024年 113卷 108192页

作者： Xie, Bin Chen, Qiong Dai, Ziyu Jiang, Chen Sun, Jingyi Guan, Anqi Chen, Xi Cent South Univ Xiangya Hosp Ctr Resp Med Changsha 410008 Hunan Peoples R China Cent South Univ Xiangya Hosp Dept Geriatr Resp Med Changsha 410008 Peoples R China Cent South Univ Xiangya Hosp Branch Natl Clin Res Ctr Resp Dis Natl Key Clin Specialty Changsha 410008 Hunan Peoples R China Hunan Engn Res Ctr Intelligent Diag & Treatment Re Changsha 410008 Hunan Peoples R China Cent South Univ Xiangya Hosp Xiangya Lung Canc Ctr Changsha 410008 Peoples R China Cent South Univ Xiangya Hosp Natl Clin Res Ctr Geriatr Disorders Changsha 410008 Peoples R China

Ferroptosis is a newly identified form of non-apoptotic programmed cell death resulting from iron-dependent lipid peroxidation. It is controlled by integrated oxidation and antioxidant systems. Ferroptosis exerts a crucial effect on the carcinogenesis of several cancers, including pulmonary cancer. Herein, a ferroptosisassociated gene signature for lung cancer prognosis and diagnosis was identified using integrative bioinformatics analyses. From the FerrDB database, 256 ferroptotic regulators and markers were identified. Of these, 25 exhibited differential expression between lung cancer and non-cancerous samples, as evidenced by the GSE19804 and GSE7670 datasets from the GEO database. Utilizing lasso Cox regression analysis on TCGA-LUAD data, a potent 3-gene risk signature comprising CAV1, RRM2, and EGFR was established. This signature adeptly differentiates various survival outcomes in lung cancer patients, including overall survival and disease-specific intervals. Based on the 3-gene risk signature, lung cancer patients were categorized into high-risk and lowrisk groups. Comparative analysis revealed 69 differentially expressed genes between these groups, with UBE2Z significantly associated with overall survival in TCGA-LUAD. UBE2Z was found to be upregulated in LUAD tissues and cells compared to normal controls. Functionally, the knockdown of UBE2Z curtailed aggressive behaviors in LUAD cells, including viability, migration, and invasion. Moreover, this knockdown led to a decrease in the mesenchymal marker vimentin while elevating the epithelial marker E-cadherin within LUAD cell lines. In conclusion, the ferroptosis-associated 3-gene risk signature effectively differentiates prognosis and clinical features in patients with lung cancer. UBE2Z was identified through this model, and it is upregulated in LUAD samples. Its knockdown inhibits aggressive cellular behaviors, suggesting UBE2Z's potential as a therapeutic target for lung cancer treatment.

关键词： Lung cancer Ferroptosis lasso algorithm Ubiquitin conjugating enzyme E2 Z (UBE2Z)

来源：评论

学校读者我要写书评

暂无评论

Analysis of Influencing Factors of Taxation and Fiscal Revenue in Anyang City based on Ridge Regression and lasso Regression

World Scientific Research Journal

引用

World Scientific Research Journal 2020年第10期6卷 203-211页

作者： Haoran Fu Huahui Li

In this paper, the ridge regression analysis and lasso algorithm are compared to study the influence of tax revenue on fiscal revenue in Anyang Economic and Technological Development Zone under the influence of tax reduction and fee reduction policy. The influence of different enterprises on the city's fiscal revenue is obtained: the industries that affect the fiscal revenue mainly include construction industry and materials and products industry. It provides a reference for the investment and reform of enterprises in the next step, thus making the related financial work of the government more convenient and credible.

关键词： Tax revenue Fiscal revenue ridge regression analysis lasso algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhanced Error Compensation Method for Robotic Machining System via Two-Step Kinematic Parameters Calibration 9

Enhanced Error Compensation Method for Robotic Machining Sys...

引用

9th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)

作者： Yan, Huiteng Ma, Jianwei Chen, Weinan Yue, Haibao Li, Linyu Liu, Wei Dalian Univ Technol State Key Lab High Performance Precis Mfg Dalian Peoples R China Shenyang Aircraft Corp Shenyang Peoples R China Taiyuan Heavy Ind Co Ltd Technol Ctr Taiyuan Peoples R China

ISBN: (纸本)9798350352573

Mobile robot machining system offers an effective solution for the integrated machining of large-scale components. However, the lower absolute positioning accuracy of industrial robots poses a significant challenge to achieving high-precision manufacturing of such components. Identifying robot kinematic parameters and compensating for motion errors represent effective approaches to enhancing the absolute positioning accuracy of robots. In this paper, we propose a two-step method for identifying robot kinematic parameters, which combines the lasso algorithm with the IPSO algorithm, aiming to improve the absolute positioning accuracy of robotic machining systems for large-scale components. Initially, values of robot kinematic parameter deviations are obtained by applying the lasso algorithm. These values then serve as initial particles for the IPSO algorithm, enabling more efficient and accurate kinematic parameter identification. Subsequently, the kinematic parameters of the robot machining system are efficiently calibrated. Experimental results demonstrate that the maximum absolute positioning error is improved by over 90% before calibration, demonstrating the validity of the proposed method in enhancing the absolute positioning accuracy of industrial robots.

关键词： Mobile robotic machining system Kinematic parameters calibration Positioning accuracy lasso algorithm IPSO algorithm

来源：评论

学校读者我要写书评

暂无评论

Joint Variable Selection and Classification with Immunohistochemical Data

引用

BIOMARKER INSIGHTS 2009年第unknown期4卷 103-110页

作者： Ghosh, Debashis Chakrabarti, Ratna Penn State Univ Dept Stat Wartik Lab 514A University Pk PA 16802 USA Univ Cent Florida Dept Mol Biol & Microbiol Orlando FL 32826 USA

To determine if candidate cancer biomarkers have utility in a clinical setting, validation using immunohistochemical methods is typically done. Most analyses of such data have not incorporated the multivariate nature of the staining profiles. In this article, we consider modelling such data using recently developed ideas from the machine learning community. In particular, we consider the joint goals of feature selection and classification. We develop estimation procedures for the analysis of immunohistochemical profiles using the least absolute selection and shrinkage operator. These lead to novel and flexible models and algorithms for the analysis of compositional data. The techniques are illustrated using data from a cancer biomarker study.

关键词： antibody lasso algorithm L-1 penalty tissue microarray protein

来源：评论

学校读者我要写书评

暂无评论

A New Data Preprocessing Technique Based on Feature Extraction and Clustering for Complex Discrete Temperature Data

A New Data Preprocessing Technique Based on Feature Extracti...

引用

6th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Yang, Lingzhi Ban, Xiaojuan Chen, Zhe Guo, Hongyue Univ Sci & Technol 30 Xueyuan Rd Beijing 100083 Peoples R China Qingdao Hisense Grp Co Ltd 17 Donghai West Rd Qingdao 266000 Peoples R China

LF(LADLE FURNACE) refining technology is the key process to regulate the temperature in steelmaking process. To predict the end temperature of molten steel in LF, this paper proposes a new data preprocessing technique based on feature extraction and clustering. Firstly,random forest algorithm was used to predict the temperature, the predictive hit rate of error within +/- 10 degrees C was 73.18%. The lasso algorithm and K-means algorithm was used for feature extraction and clustering. After improvement, the prediction accuracy of the LF end temperature of error within +/- 10 degrees C was about 88.16%. The results show that this improvement has high prediction accuracy in the prediction about the end temperature of molten steel in LF refining. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： LF refining furnace Complex discrete temperature Random forest algorithm K-means lasso algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on Financial Early Warning of Listed Companies Based on lasso-logistic Model

Research on Financial Early Warning of Listed Companies Base...

引用

第三届经济与企业管理国际学术会议

作者： Yutong Han Qi Sun Zhuoxi Yu School of Management Science and Information Engineering Jilin University of Finance and Economics Jilin Province Key Laboratory of Fintech

The financial status is an important factor affecting the survival and development of enterprises. When we conduct financial early-warning model analysis, the selection of index variables and the estimation of model parameters directly affect the prediction accuracy of the early-warning model. lasso is a variable selection method for shrinkage estimation. By constructing a penalty functions to realize variety selection and retaining the advantages of subset shrinkage;lasso can be applied to time series, high-dimensional graphics discrimination and selection. In this paper, the lasso method and the Logistic model are combined to construct an early-warning model reflecting the financial status within the enterprise. The experimental results show that the model can effectively select relatively important influencing factors and also has good predictive evokes.

关键词： Financial warning lasso algorithm Logistic model Variable selection

来源：评论

学校读者我要写书评

暂无评论

Využití technik genetických algoritmů a dolování z dat v testování paralelních programů s využitím vkládání šumu

Využití technik genetických algoritmů a dolování z dat...

引用

作者： Šimková, Hana Brno University of Technology

Tato práce navrhuje zlepšení výkonu testování programů použitím technik dolování z dat a genetických algoritmů při testování paralelních programů. Para... 详细信息

Tato práce navrhuje zlepšení výkonu testování programů použitím technik dolování z dat a genetických algoritmů při testování paralelních programů. Paralelní programování se v posledních letech stává velmi populárním i přesto, že toto programování je mnohem náročnějsí než jednodušší sekvenční a proto jeho zvýšené používání vede k podstatně vyššímu počtu chyb. Tyto chyby se vyskytují v důsledku chyb v synchronizaci jednotlivých procesů programu. Nalezení takových chyb tradičním způsobem je složité a navíc opakované spouštění těchto testů ve stejném prostředí typicky vede pouze k prohledávání stejných prokládání. V práci se využívá metody vstřikování šumu, která vystresuje program tak, že se mohou objevit některá nová chování. Pro účinnost této metody je nutné zvolit vhodné heuristiky a též i hodnoty jejich parametrů, což není snadné. V práci se využívá metod dolování z dat, genetických algoritmů a jejich kombinace pro nalezení těchto heuristik a hodnot parametrů. V práci je vedle výsledků výzkumu uveden stručný přehled dalších Technik testování paralelních programů.

关键词： testování paralelní programy dolování z dat genetické algoritmy AdaBoost lasso algoritmus vstřikování šumu testing concurrent programs data mining genetic algorithms AdaBoost lasso algorithm noise injection Text

来源：评论

学校读者我要写书评

暂无评论

DUAL-SPARSITY PRESERVING PROJECTION

引用

Transactions of Nanjing University of Aeronautics and Astronautics 2012年第3期29卷 284-288页

作者：闫雪梅张丽梅郭文彬聊城大学数学科学学院

Sparsity preserving projection（SPP） is a popular graph-based dimensionality reduction（DR） method, which has been successfully applied to solve face recognition recently. SPP contains natural discriminating information by preserving sparse reconstruction relationship of data sets. However, SPP suffers from the fact that every new feature learned from data sets is linear combinations of all the original features, which often makes it difficult to interpret the results. To address this issue, a novel DR method called dual-sparsity preserving projection （DSPP） is proposed to further impose sparsity constraints on the projection directions of SPP. Specifically, the proposed method casts the projection function learning of SPP into a regression-type optimization problem, and then the sparse projections can be efficiently computed by the related lasso algorithm. Experimental results from face databases demonstrate the effectiveness of the proposed algorithm.

关键词： sparsity preserving projection dimensionality reduction spectral regression lasso algorithm face recognition

来源：评论

学校读者我要写书评

暂无评论

Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant algorithms

引用

SENSORS 2023年第4期23卷 2263-2263页

作者： Burel, Gilles Radoi, Emanuel Gautier, Roland Le Jeune, Denis Univ Brest CNRS Lab STICC CS 93837 6 Ave Gorgeu F-29238 Brest 3 France ENSTA Bretagne CNRS Lab STICC 2 Rue Francois Verny F-29806 Brest 9 France

Wideband spectrum sensing is a challenging problem in the framework of cognitive radio and spectrum surveillance, mainly because of the high sampling rates required by standard approaches. In this paper, a compressed sensing approach was considered to solve this problem, relying on a sub-Nyquist or Xsampling scheme, known as a modulated wideband converter. First, the data reduction at its output is performed in order to enable a highly effective processing scheme for spectrum reconstruction. The impact of this data transformation on the behavior of the most popular sparse reconstruction algorithms is then analyzed. A new mathematical approach is proposed to demonstrate that greedy reconstruction algorithms, such as Orthogonal Matching Pursuit, are invariant with respect to the proposed data reduction. Relying on the same formalism, a data reduction invariant version of the lasso (least absolute shrinkage and selection operator) reconstruction algorithm was also introduced. It is finally demonstrated that the proposed algorithm provides good reconstruction results in a wideband spectrum sensing scenario, using both synthetic and measured data.

关键词： Xsampling modulated wideband converter compressed sensing data reduction OMP algorithm lasso algorithm wideband spectrum sensing

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：