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Research on Data Mining algorithm of Meteorological Observation Based on Data Quality Control algorithm

WIRELESS PERSONAL COMMUNICATIONS 2018年第2期102卷 2077-2089页

作者： Qing-Dao-Er-Ji, Ren Li, Na Inner Mongolia Univ Technol Sch Informat Engn Hohhot 010051 Peoples R China Inner Mongolia Univ Technol Sch Sci Hohhot 010051 Peoples R China Inner Mongolia Univ Sch Comp Sci Hohhot Peoples R China

The quality of meteorological observation data directly affects the weather forecast and the accuracy of climate prediction. The traditional quality control algorithm is not sensitive to the abnormal changes of the elements and can't meet the needs of the quality control work. Therefore, based on the data mining algorithm, this paper further studied the quality control of meteorological data from two aspects of time correlation and factor correlation. Two different methods of quality control for meteorological observation data were proposed. One is the quality control method of time correlated meteorological observations based on the characteristics of chaos (potential trend and regularity) and the support vector machine algorithm. The other is the quality control method of factor correlated meteorological observations based on BP neural network and the characteristics of different elements. Combining the complementarity and relevance between the two methods, a set of comprehensive quality control scheme is set up. The experimental results show that the proposed scheme can effectively simulate the weather observation data and detect the anomaly value.

关键词： svm algorithm BP neural network Meteorological observation Quality control Data mining algorithm

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Evaporation residue cross sections of superheavy nuclei based on optimized nuclear data

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Chinese Physics C 2023年第12期47卷 124-130页

作者：李佳星张鸿飞 School of Physics Xi’an Jiaotong UniversityXi’an 710049China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China

This study proposes an optimized method for estimating atomic nucleus masses by combining the finiterangedroplet model (FRDM) with the support vector machine algorithm. The optimization process significantly improvesthe accuracy of the FRDM by reducing the root mean square error from 0.606 to 0.253 MeV. The optimizedmass data obtained from this method are then used to calculate the evaporation residue cross-sections (ERCSs) forfusion-evaporation reactions, employing the di-nuclear system model. The experimental results for the 48Ca+238U reactionare relatively well reproduced using these optimized mass data. Additionally, the study investigates the impactof mass uncertainties on fusion and survival probabilities. By considering the mass uncertainties, the ERCSs fornew elements 119 and 120 are predicted based on the obtained optimized mass data.

关键词： superheavy nuclei svm algorithm DNS model evaporation residue cross-sections

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Harmonic Spectrum Analysis of the Output Voltage and Grid-Side Filter Design for Three-Level Voltage-Source Converters

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2017年第5期12卷 786-793页

作者： Li, Hongliang Jiang, Jianguo Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

Because of the complex arithmetic computation of the traditional three-level space vector modulation (svm), its hardware implementation is difficult to achieve. Moreover, the normal approach of acquiring the output voltage harmonic is sensitive to the simulation step, measurement accuracy, system noise, etc. This paper proposes a new method for analyzing the voltage harmonic spectrum of the three-level converter. Furthermore, the minimum value of the inductance of the grid-side filters is designed on this basis. First, the formulas of the duty cycle and the modulation waveform of the three-level svm algorithm are easily derived using some simple linear calculations under the 120 degrees coordinate. Then, considering the characteristics of the digital circuits, the harmonic components of the output voltage are calculated using Fourier analysis. Third, a precise analytical design of the minimum inductance value of grid-connected converters with LCL and L input filters is presented. Finally, the validity of this approach is verified by using experimental results. (C) 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： harmonic analysis grid-side filter design svm algorithm three-level converter 120 degrees coordinate

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Multi-dimensional Data Optimal Classification algorithm for Quality Evaluation of Distance Teaching in Universities

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MOBILE NETWORKS & APPLICATIONS 2023年第3期28卷 889-899页

作者： Chen, Feng Gadekallu, Thippa Reddy Xizang Minzu Univ Coll Informat Engn Xianyang 712082 Peoples R China Zhongda Grp Jiaxing 314312 Peoples R China Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Vellore Inst Technol Sch Informat Technol & Engn Vellore 632014 India Jiaxing Univ Coll Informat Sci & Engn Jiaxing 314001 Peoples R China Lovely Profess Univ Div Res & Dev Phagwara India

In order to effectively extract the multi-dimensional data of teaching quality evaluation and accurately evaluate the quality of network distance teaching quality in universities, an optimal classification algorithm for network distance teaching quality evaluation is proposed. From the perspectives of teaching attitude, teaching skill, teaching skill, teaching content, teaching method and means, the quality evaluation indices of network distance teaching is designed. Combined with the evaluation indices, the multi-dimensional data mining method based on OLAP technology is used to mine the required multi-dimensional data of teaching quality evaluation in the distance teaching data warehouse of universities, the required multi-dimensional data is input into svm algorithm to solve the optimal classification hyperplane of multi-dimensional data and implement the optimal classification of multi-dimensional data. The quality of distance teaching is evaluated by improving the salp group algorithm and setting the penalty factor and kernel function of svm algorithm. The experimental results show that the classification accuracy of this method for multidimensional data is over 90%, and the evaluation accuracy is as high as 99%, it can extract multi-dimensional teaching quality evaluation from the network distance teaching data, which has good classification effect and can improve the accuracy of network distance teaching quality evaluation.

关键词： OLAP Technology svm algorithm University Network Distance Teaching Quality Evaluation and Evaluation Index

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An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第1-4期69卷 583-593页

作者： Liu, Wei Tu, Xianming Jia, Zhenyuan Wang, Wenqiang Ma, Xin Bi, Xiaodan Dalian Univ Technol Key Lab Precis & Nontradit Machining Technol Minist Educ Sch Mech Engn Dalian 116024 Peoples R China

Microscopic vision system has been employed to measure the surface roughness of micro-heterogeneous texture in deep hole, by virtue of frequency domain features of microscopic image and back-propagation artificial neural network optimized by genetic algorithm. However, the measurement accuracy needs to be improved for engineering application. In this paper, we propose an improved method based on microscopic vision to detect the surface roughness of R-surface in the valve. Firstly, the measurement system for the roughness of R-surface in deep hole is described. Thereafter, the surface topography images of R-surface are analyzed by the gray-level co-occurrence matrix (GLCM) method, and several features of microscopic image, which are nearly monotonic with the surface roughness, are extracted to fabricate the prediction model of the roughness of R-surface accurately. Moreover, a support vector machine (svm) model is presented to describe the relationship of GLCM features and the actual surface roughness. Finally, experiments on measuring the surface roughness are conducted, and the experimental results indicate that the GLCM-svm model exhibits higher accuracy and generalization ability for evaluating the microcosmic surface roughness of micro-heterogeneous texture in deep hole.

关键词： Surface roughness Gray-level co-occurrence Textural analysis Genetic algorithm svm algorithm Micro-heterogeneous texture

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Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method

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OPEN GEOSCIENCES 2024年第1期16卷

作者： Zhao, Longhui Qin, Yigen Yang, Genlan Zhang, Wuzhou Lu, Kunpeng Guizhou Water Resources & Hydropower Survey Design Guiyang 550004 Peoples R China Sun Yat Sen Univ Sch Earth Sci & Engn Guangdong Prov Key Lab Geodynam & Geohazards Guangzhou Peoples R China Guizhou Univ Coll Resource & Environm Engn Guiyang 550025 Peoples R China Guizhou Zhiheng Engn Survey & Design Consulting Co Guiyang 550004 Peoples R China

This study aims to develop a comprehensive method for automated rockfall mapping and susceptibility assessment using unmanned aerial vehicle (UAV) tilt photography and the support vector machine (svm) algorithm. By employing Jinzhong Town in the mountainous forest landscapes of Southwest China as a case study, we leverage photogrammetry principles and computer vision algorithms to generate high-precision, high-resolution digital surface models (DSMs), and digital orthophoto maps through a UAV remote sensing system. The rockfall inventory is accurately and automatically mapped using the object-based classification method and svm algorithm. The automated rockfall identification method achieves a 93% accuracy with a Kappa coefficient of 0.7967. Statistical analyses of spatial distribution characteristics reveal a significant correlation between rockfall volume and area with a correlation coefficient (R-2) of 0.92316 under logarithmic coordinates. In addition, a power function relationship describes the link between rockfall volume and slope height (R-2 = 0.87), while the relationship with sliding distance is characterized by a weaker linear correlation (R-2 = 0.65). Rockfall runout distance also shows a significant linear correlation with slope height (R-2 = 0.79) but exhibits a less-pronounced association with rockfall volume (R-2 = 0.58). The svm model employed to assess rockfall susceptibility indicates high accuracy (area under the curve = 0.896), affirming its efficacy in rockfall susceptibility assessment. Our findings underscore the utility of UAV remote sensing for rockfall information extraction and susceptibility evaluation, particularly in challenging mountainous forest environments characterized by intricate topography and geological complexities.

关键词： UAV object-based image analysis svm algorithm rockfall susceptibility evaluation mountainous forest

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Fall Monitoring System Based on Wearable Device and Improved KNN

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2024年第4期58卷 366-378页

作者： Tan, Diyuan Yao, Binbin Wang, Zhe Henan Agr Univ Coll Landscape Architecture & Art Zhengzhou 450002 Peoples R China Zhongyuan Inst Sci & Technol Sch Electromech Engn Xuchang 461000 Peoples R China

For the elderly, falls can be extremely fatal. However, due to the physical decline of the elderly, it is difficult to avoid falls. Therefore, to the greatest extent feasible lessen the harm that falls on the elderly inflict, so that they can be found in the first time of falls, this study based on wearable devices, proposed a fall monitoring system using an improved K-nearest neighbor algorithm. The improved fuzzy K-nearest neighbor algorithm combined with support vector machine algorithm is applied to improve the efficiency and accuracy of the algorithm, and reduce the false positive rate and false negative rate as much as possible. The suggested model's average precision in the simulation experiment is 97.5%. The specificity was 97.6%. The sensitivity was 97.5%. The convergence performance is also good, 24 iterations can reach the optimal. In the actual experiment, the average accuracy reached 98.7%;The false alarm rate is only 0.7%;The negative rate was 2.5%;Its performance is superior to other two algorithms. This shows that the proposed method has excellent accuracy, false positive rate and false negative rate in practical application, which has important significance for the health and safety of the elderly.

关键词： fall detection system KNN algorithm svm algorithm fuzzy set

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An Enhanced Recursive Firefly algorithm for Informative Gene Selection

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019年第2期10卷 21-33页

作者： Dif, Nassima Elberrichi, Zakaria Univ Djillali Liabes Sidi Bel Abbes EEDIS Lab Sidi Bel Abbes Algeria

Feature selection is the process of identifying good performing combinations of significant features among many possibilities. This preprocess improves the classification accuracy and facilitates the learning task. For this optimization problem, the authors have used a metaheuristics approach. Their main objective is to propose an enhanced version of the firefly algorithm as a wrapper approach by adding a recursive behavior to improve the search of the optimal solution. They applied svm classifier to investigate the proposed method. For the authors experimentations, they have used the benchmark microarray datasets. The results show that the new enhanced recursive FA (RFA) outperforms the standard version with a reduction of dimensionality for all the datasets. As an example, for the leukemia microarray dataset, they have a perfect performance score of 100% with only 18 informative selected genes among the 7,129 of the original dataset. The RFA was competitive compared to other state-of-art approaches and achieved the best results for CNS, Ovarian cancer, MLL, prostate, Leukemia_4c, and lymphoma datasets.

关键词： Classification Data Mining Data Preprocessing Data Reduction Feature Selection Firefly algorithm Metaheuristic Microarray Datasets Recursion svm algorithm

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Diagnosis of thyroid neoplasm using support vector machine algorithms based on platelet RNA-seq

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ENDOCRINE 2021年第3期72卷 758-783页

作者： Shen, Yuling Lai, Yi Xu, Dong Xu, Le Song, Lin Zhou, Jiaqing Song, Chengwen Wang, Jiadong Shanghai Jiao Tong Univ Sch Med Renji Hosp Dept Head & Neck Surg 160 Pujian Rd Shanghai 200127 Peoples R China Fun Med Pharmaceut Technol Shanghai Co Ltd RM A310115 Xinjunhuan Rd Shanghai 201100 Peoples R China

Objective To assess the capacity of support vector machine (svm) algorithms that are developed based on platelet RNA-seq data in identifying thyroid neoplasm patients and differentiating patients with thyroid adenomas, papillary thyroid cancer and metastasized papillary thyroid cancer. Methods Platelets were collected and isolated from 109 patients and 63 healthy controls. RNA-seq was performed to find transcripts with differential levels. Genes corresponding to these altered transcripts were identified using R packages. All samples were subsampled into a training set and a validation set. Two svm algorithms were developed and trained with the training set, using the genes with differential transcript levels (GDTLs) as classifiers, and validated with the validation set. GO and KEGG pathway enrichment analysis were performed using the R package clusterProfiler. Results We detected 765 GDTLs (442 up-regulated and 323 down-regulated) in platelets of patients and healthy controls. The algorithm identifying thyroid neoplasm patients achieved an accuracy of 97%, with an AUC (area under curve) of 0.998. The other algorithm differentiating patients with multiclass thyroid neoplasms had an average accuracy of 80.5%. GO analysis showed that GDTLs were strongly involved in biological processes such as neutrophil degranulation, neutrophil activation, autophagy and regulation of multi-organism process. KEGG pathway enrichment analysis revealed that GDTLs were mainly enriched in NOD-like receptor signaling pathway and pathways in endocytosis, osteoclast differentiation, human cytomegalovirus infection and tuberculosis. Conclusion Our results indicated that the combination of svm algorithms and platelet RNA-seq data allowed for thyroid neoplasm diagnostics and multiclass thyroid neoplasm classification.

关键词： Thyroid neoplasm svm algorithm Platelet RNA-seq Bioinformatics analysis

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Research on the analysis method of power system relay protection action characteristics based on fault recording data

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JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2024年第3期17卷

作者： Guo, Wen Hou, Xiaohu China Yangtze Power Co Ltd Xiluodu Hydropower Plant Zhaotong 657300 Yunnan Peoples R China

The action characteristics of power system relay protection devices can well analyze whether the relevant actions are correct. An analysis method of relay protection action characteristics in power system based on fault recording data is designed. After analyzing the relay device structure and protection action characteristics and requirements, the power system relay protection action record data recorded by the fault recorder;The second order RC low-pass filter is used to preprocess the recorded data;The filtered data is input into the analysis model of relay protection action characteristics based on svm algorithm. The svm algorithm classifies whether the relay protection action characteristics recorded by the filtered fault recording data meet the expectations, and completes the analysis of power system relay protection action characteristics. The experimental results show that this method can effectively analyze the operation characteristics of power system relay protection, and can accurately check whether the relay protection device can operate correctly.

关键词： Fault recording data Power system Relay protection Action characteristic analysis svm algorithm Low pass filter

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