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Outlier Identification of Concrete Dam Displacement Monitoring Data Based on WAVLET-dbscan-IFRL

WATER 2025年第5期17卷 716-716页

作者： Fang, Chunhui Wang, Xue Hu, Weixing He, Xiaojun Huang, Zihui Gu, Hao Zhejiang Univ Water Resources & Elect Power Res Inst Water Sci Hangzhou 310018 Peoples R China Zhejiang Tongji Vocat Coll Sci & Technol Coll Water Conservancy Engn Hangzhou 311231 Peoples R China Water Affairs Bur Wuyi Cty Jinhua 321200 Peoples R China Nat Resources & Planning Bur Wuyi Cty Jinhua 321200 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Monitoring data outliers comprises isolated mode outliers, cluster mode outliers, and normal points. To identify and distinguish the data hopping problems caused by outliers and environmental mutations in the displacement monitoring data of concrete AMS, this paper proposes a method based on wavelet transform, dbscan clustering algorithm combined with isolated forest and reinforcement learning algorithm to identify outliers in concrete dam monitoring data. In this paper, the trend line of measuring point data are extracted by the wavelet transform algorithm, and the residual data are obtained by subtracting it from the original process line. Subsequently, the dbscan clustering algorithm divides the residual data according to density. Therewith, the outlier scores of different data clusters are calculated, the iterative Q values are updated, and the threshold values are set. The data exceeding the threshold are finally marked as outliers. Finally, the water level and displacement data were compared by drawing the trend to ensure that the water level change did not cause the final identified concrete dam displacement data outliers. The results of the example analysis show that compared with the other two outlier detection methods ("Wavelet transform combined with dbscan clustering" or "W-D method", "Wavelet transform combined with isolated forest method" or "W-IF method"). The method has the lowest error rate and the highest precision rate, recall rate, and F1 score. The error rate, precision rate, recall rate, and F1 score were 0.0036, 0.870, 1.000, and 0.931, respectively. This method can effectively identify data jumps caused by an environmental mutation in deformation monitoring data, significantly improve the accuracy of outlier identification, reduce the misjudgement rate of outliers, and have the highest detection accuracy.

关键词： concrete dam displacement monitoring data outlier detection wavelet transform dbscan clustering algorithm isolation forest algorithm reinforcement learning

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Extension of a clustering identification approach to multivariable piecewise affine systems: Application to an industrial dryer

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2024年第6期238卷 1037-1049页

作者： Zeineb, Lassoued Kamel, Abderrahim Univ Gabes Natl Sch Engineers Gabes Numer Control Ind Proc Gabes Tunisia Univ Gabes Natl Sch Engineers Gabes Numer Control Ind Proc St Omar Ibn Khattab Gabes 6029 Tunisia

The main objective of this paper is the extension of the clustering-based identification approach to Multi-Input Multi-Output (MIMO) PieceWise Affine systems (PWA). This approach is performed by three main steps which are data clustering, parameters matrices estimation and regions computing. Data clustering is the most important step because the performances depend on the results given by the used clustering algorithm. In the case of MIMO PWA systems, we should cluster matrices of parameters which are considered high dimensional data. However, most of the conventional clustering algorithms are not efficient since the similarity assessment which is based on the distances between objects is fruitless in high dimension space. Therefore, we propose an extension of the dbscan (Density Based Spatial clustering of Applications with Noise) clustering technique to identify MIMO PWA systems. The simulation results presented in this paper illustrate the performance of the proposed method. An application to an industrial dryer of Di-Calcium Phosphate (DCP) is also presented in order to strengthen the simulation results.

关键词： MIMO process PWA model dbscan clustering algorithm identification

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A precise crop row detection algorithm in complex farmland for unmanned agricultural machines

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BIOSYSTEMS ENGINEERING 2023年第1期232卷 1-12页

作者： Ruan, Zhiwen Chang, Penghao Cui, Shangqing Luo, Jiaqi Gao, Rui Su, Zhongbin Northeast Agr Univ Inst Elect & Informat Harbin 150030 Peoples R China

Crop row detection is an important part of precision agriculture. Therefore, an unmanned agricultural machine crop row detection method based on YOLO-R is proposed. This method can solve the problem that traditional image processing methods are easily affected by weeds, light and other factors when extracting crop feature points. First, the YOLO-R object detection algorithm was used to obtain the crop position information, and then, the number of crop rows in the image and the crop in each crop row were obtained by the dbscan clustering algorithm. Finally, the function expression for each crop row was obtained by using the least squares method. The experimental results show that the AP values of YOLO-R are 91.69%, 95.34% and 89.13% on the seven-day, 14-day, and 21-day rice datasets, respectively. When the proposed algorithm's number of parameters was only 12.31% of that of YOLOv4 and the FPS was 17.54 higher than that of YOLOv4, the AP value was only 2.2% lower. The accuracy values of crop row detection algorithm are 93.91%, 95.87% and 89.87% on the seven-day, 14-day, and 21-day rice datasets, respectively, which indicates that the algorithm in this paper can effectively identify crop lines. & COPY;2023 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Crop row detection YOLO-R object detection algorithm dbscan clustering algorithm Least square method

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Automatic Extraction of Forest Inventory Variables at the Tree Level by Using Smartphone Images to Construct a Three-Dimensional Model

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FORESTS 2023年第6期14卷 1081-1081页

作者： Song, Jiayin Huang, Qiqi Zhao, Yue Song, Wenlong Fan, Yiming Lu, Chao Northeast Forestry Univ Coll Comp & Control Engn Harbin 150040 Peoples R China

This paper focuses on the current urgent demand for the accurate measurement of forest inventory variables in the fields of forestry carbon sink measurement, ecosystem research, and forest resource conservation, and proposes the use of images to construct a three-dimensional measurement model of forest inventory variables, which is a new method to realize the automatic extraction of forest inventory variables. This method obtains sample site information by using high-definition images taken in the forest by a smartphone, which significantly improves the field operation efficiency and simple operation, and effectively alleviates the problems of long field operation times, complicated operations, and expensive equipment used by current methods for obtaining forest inventory variables. We propose to optimize the Eps parameters of the dbscan algorithm based on the MVO algorithm for point cloud clustering to obtain single wood point clouds, which improves the accuracy of the model and can effectively solve the problem of large interference from human factors. The scale coefficients of the image and the actual model are obtained by the actual measurement of tree height and diameter at breast height to complete the construction of the three-dimensional measurement model of the stand and are then combined with the AdQSM algorithm to realize the automatic extraction of forest inventory variables, which provides a new interdisciplinary method for the comprehensive extraction of forest inventory variables. The accuracy of the model measured in the experimental sample site of Fraxinus mandshurica Rupr was as follows: the absolute error of tree height measurement ranged from 0.05 to 0.37 m, the highest relative error of measurement was 2.03%, and the average relative error was 1.53%;for the absolute error of diameter at breast height, measurement ranged from 0.007 to 0.057 m, the highest relative error of measurement was 7.358%, and the average relative error was 3.616%. The met

关键词： dbscan clustering algorithm forest inventory variables 3D measurement model point cloud filtering point cloud segmentation

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Extraction of terrain ridge lines and valley lines based on agglomeration analysis of terrain points: a cluster analysis method

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EARTH SCIENCE INFORMATICS 2023年第1期16卷 345-356页

作者： Zhang, Cheng Dou, Wanfeng Pang, Yuan Jiangsu Open Univ Informat Construction Dept Nanjing 210036 Peoples R China Nanjing Normal Univ Sch Comp & Elect Informat Nanjing 210023 Peoples R China Nanjing Normal Univ Jiangsu Intelligent Informat Technol & Software En Nanjing 210023 Peoples R China

Terrain feature extraction is one of the critical issues in geographic information science. As important terrain feature lines, ridge lines and valley lines play an important role in hydrological analysis, terrain reconstruction and automatic integration of contour lines. But the extraction of terrain feature lines is complicated and time-consuming task. In this paper, a terrain feature line extraction method is proposed based on clustering technique. The terrain feature points are automatically extracted according to the agglomeration of terrain points, and the similar points are automatically identified according to the dbscan clustering algorithm. The points with high similarity are clustered along the direction of ridge or valley, and the whole terrain will be clustered into multiple sub-regions. The nearest sub-regions are found by calculating the minimum distance between these sub-regions, the adjacent sub-regions are connected orderly by their center line to obtain terrain feature lines. Compared with other methods, the cluster analysis method in this paper has simple process and high efficiency.

关键词： Terrain feature line extraction dbscan clustering algorithm Ridge line Valley line

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Network Anomaly Detection algorithm Based on Deep Learning and Data Mining 24

Network Anomaly Detection Algorithm Based on Deep Learning a...

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3rd International Conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Li, Yiting RunJian Co Ltd Guangzhou 510000 Guangdong Peoples R China

ISBN: (纸本)9798400716959

In this paper, we propose a new detection method for network anomaly detection, a key problem in the field of cyber security, which combines deep learning feature extraction and dbscan clustering algorithm. First, in terms of data processing and feature learning, we employ deep learning models CNN and RNN to automatically extract useful features from complex network traffic data. These advanced features can represent network behavior more accurately and provide richer information for subsequent clustering and anomaly detection. Then, using the dbscan algorithm, we can not only effectively identify normal behavioral patterns, but also accurately detect anomalies and potential threats. In the experimental part, by evaluating on the publicly available KDD Cup 99 dataset, the method in this paper demonstrates its superiority in key metrics such as accuracy and recall.

关键词： Network anomaly detection method Deep learning feature extraction dbscan clustering algorithm data process Identify behavioral pattern

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Intelligent Blind Source Separation Technology Based on OTFS Modulation for LEO Satellite Communication

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China Communications 2022年第7期19卷 89-99页

作者： Chengjie Li Lidong Zhu Cheng Guo Tao Liu Zhen Zhang The Key Laboratory for Computer Systems of State Ethnic Affairs Commission School of Computer Science and Technology(Southwest Minzu University)Chengdu 610000China National Key Laboratory of Science and Technology on Communications(University of Electronic Science and Technology of China) Chengdu 610000China College of Computer Science(Sichuan University) Chengdu 610000China

In LEO(Low Earth Orbit)satellite communication system,the orbit height of the satellite is low,the transmission delay is short,the path loss is small,and the frequency multiplexing is more ***,it is an unavoidable technical problem of the significant Doppler effect caused by the interference between satellite networks and the high-speed movement of the satellite relative to the *** order to improve the target detection efficiency and system security of LEO satellite communication system,blind separation technology is an effective method to process the collision signals received by *** of the signal has good sparsity in Delay-Doppler domain,in order to improve the blind separation performance of LEO satellite communication system,orthogonal Time-Frequency space(OTFS)modulation is used to convert the sampled signal to Delay-Doppler *** clustering algorithm is used to classify the sparse signal,so as to separate the original mixed ***,the simulation results show that the method has a good separation effect,and can significantly improve the detection efficiency of system targets and the security of LEO satellite communication system network.

关键词： LEO Doppler effect Delay-Doppler domain OTFS dbscan clustering algorithm

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Urban Greenway Planning and Designing Based on MGWR and the Entropy Weight Method

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APPLIED SCIENCES-BASEL 2024年第24期14卷 11670页

作者： Li, Weijia Ji, Xinge Bai, Hua Changan Univ Sch Architecture Xian 710064 Peoples R China Univ Shaanxi Prov Engn Res Ctr Collaborat Planning Low Carbon Urban Xian 710061 Peoples R China

Travelers' attention to high-quality human habitats is increasing, and the role of urban greenways in improving the quality of travelling spaces has also been appreciated. This research aims at making the weight calculation of suitability more scientific and reasonable, clustering the shared bicycle travelling OD points according to suitability, and analyzing the distribution of OD points. Taking Xiamen as an example, multiscale geographically weighted regression and entropy weight methods were used to calculate the weights of variables using multi-source big data. The clustering of origin-destination (OD) points for shared bicycle travel are identified using the dbscan clustering algorithm, which can provide accurate support for greenway planning and shared bicycle placement. The results show that the density of tourist attractions, POI entropy index, road density, and intermediate are four important factors affecting the suitability of greenways. The clustering results of the shared bicycle OD points show that the high-aggregation areas of origin and destination points are located in the northeast and southwest directions as well as west and east directions. This study provides a theoretical and modelling analysis reference for greenway planning and design.

关键词： urban greenway entropy weight method multiscale geographic weighted regression suitability origin-destination of shared bicycle dbscan clustering algorithm Xiamen

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Extraction of Arbors from Terrestrial Laser Scanning Data Based on Trunk Axis Fitting

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FORESTS 2024年第7期15卷 1217页

作者： Liu, Song Deng, Yuncheng Zhang, Jianpeng Wang, Jinliang Duan, Di Yunnan Normal Univ Fac Geog Kunming 650500 Peoples R China Environm Remote Sensing Univ Yunnan Kunming 650500 Peoples R China Ctr Geospatial Informat Engn & Technol Yunnan Prov Remote Sensing Res Lab Kunming 650500 Peoples R China Southwest United Grad Sch Kunming 650092 Peoples R China

Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this study proposes a trunk axis fitting (TAF) method for arbor extraction. After separating the point cloud data by upper and lower, slicing, clustering, fitting circles, obtaining the main central axis, filtering by distance, etc. The canopy point clouds are merged with the extracted trunk point clouds to precisely separate arbors and shrubs. The advantage of the TAF method proposed in this study is that it is not affected by point cloud density or the degree of trunk curvature. This study focuses on a natural forest plot in Shangri-La City, Yunnan Province, and a plantation plot in Kunming City, using manually extracted data from a standardized dataset of samples to test the accuracy of the TAF method and validate the feasibility of the proposed method. The results showed that the TAF method proposed in this study has high extraction accuracy. It can effectively avoid the problem of trunk point cloud loss caused by tree growth curvature. The experimental accuracy for both plots reached over 99%. This study can provide certain technical support for arbor parameter extraction and scientific guidance for forest resource investigation and forest management decision-making.

关键词： Terrestrial Laser Scanning TAF method dbscan clustering algorithm trunk axis arbor extraction

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Research on Railway Connection Modes Behavior and Users Profiles Based on Passenger clustering 7

Research on Railway Connection Modes Behavior and Users Prof...

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IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Gao, Han Zhang, Qi Han, Baoming Yang, Ruixia Yu, Yiran Ran, Jiangliang Beijing Jiaotong Univ Traff & Transportat Beijing Peoples R China China RAILWAY FIRST Survey & Design Inst Grp Co L Xian Peoples R China

ISBN: (纸本)9781665460071

Due to the higher requirements of the intelligent and personalized service level in the entire travel process, Maas has become a key research area for scholars. As an important part of the full trip chain, the connection stage plays an significant role in the satisfaction of railway passengers. To provide a service that better cater to individual passengers, this paper proposes an intelligent approach for identifying profiles of railway passengers' connection preference. First, passengers were clustered into three segments by improved adaptive dbscan algorithm based on their personal attributes which captured in the questionnaire. Then, multivariate Logistic regression model is used to fit the parameter values of the passenger group's selection behavior of connection mode. According to the parameter, user profiles of the three segments are identified and verified: (i) economy-preferred passengers;(ii) convenience-preferred passengers;(iii) time-preferred passengers. This method of profile portraying is able to formulate personalized and differentiated marketing strategies by matching more accurate and efficient connection travel plans for specific railway passengers groups.

关键词： user profile connection mode choice personalized travel service dbscan clustering algorithm labels formulating Introduction Maas

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