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Comparison of K-means and fcm algorithms to optimize spatiotemporal pore pressure prediction of earth dams

RESULTS IN ENGINEERING 2024年 24卷

作者： Beiranvand, Behrang Rajaee, Taher Komasi, Mehdi Univ Qom Dept Civil Engn Qom Iran Univ Ayatollah Dept Civil Engn Ozma Boroujerdi Iran

To identify the behavior and health monitoring of dams, it is necessary to correctly interpret the results of instrumentation in different phases of construction, impounding, and operation. Therefore, the approach based on spatiotemporal prediction is presented to improve the interpretation of pore pressure behavior of Eyvashan Earth Dam. In this research, using the results of other existing healthy piezometers, a spatiotemporal distribution model is proposed using panel data, which can be effective for predicting and reconstructing missing data. The optimal spatiotemporal clustering of pore pressure changes monitoring with K-Means and Fuzzy C-means (fcm) algorithms will enable the monitoring of points of the dam where instrumentations are not designed and installed or defective instrumentations. In predicting the pore pressure of dams, the input data is classified based on the pore pressure monitoring data, but with the use of clustering algorithms, the classification after the cluster analysis steps will lead to the proper resolution of the pore pressure clustering. According to the validation results of each of the clustering algorithms, the fcm clustering algorithm has more suitable results than the K-Means algorithm in determining the pore pressure clusters. In general, fcm clustering and K-Means algorithms are suitable and efficient tools in the field of more accurate monitoring of earth dams, and by using the proposed method, the detection of unusual areas of pore pressure and the related safety diagnosis is facilitated.

关键词： Missing data Pore pressure Instrumentation Panel data K -means algorithm fcm algorithm

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Improved fcm algorithm Based on K-Means and Granular Computing

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JOURNAL OF INTELLIGENT SYSTEMS 2015年第2期24卷 215-222页

作者： Lu, Wei Jia Yan, Zhuang Zhi Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China Affiliated Hosp Nantong Univ Dept Informat Nantong 226001 Jiangsu Peoples R China

The fuzzy clustering algorithm has been widely used in the research area and production and life. However, the conventional fuzzy algorithms have a disadvantage of high computational complexity. This article proposes an improved fuzzy C-means (fcm) algorithm based on K-means and principle of granularity. This algorithm is aiming at solving the problems of optimal number of clusters and sensitivity to the data initialization in the conventional fcm methods. The initialization stage of the K-medoid cluster, which is different from others, has a strong representation and is capable of detecting data with different sizes. Meanwhile, through the combination of the granular computing and fcm, the optimal number of clusters is obtained by choosing accurate validity functions. Finally, the detailed clustering process of the proposed algorithm is presented, and its performance is validated by simulation tests. The test results show that the proposed improved fcm algorithm has enhanced clustering performance in the computational complexity, running time, cluster effectiveness compared with the existing fcm algorithms.

关键词： Fuzzy cluster K-means fcm algorithm principle of granularity

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A fuzzy clustering approach for cloud-based personalized distance music education and resource management

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SOFT COMPUTING 2024年第2期28卷 1803-1818页

作者： Jiang, Lei Jiangxi Normal Univ Sch Mus Nanchang 330027 Jiangxi Peoples R China

Advancements in information technology (IT), the Internet of Things (IoT), and other emerging technologies have led to an overflow of information and big data. The emergence of these technologies and fast internet connectivity has led to the growing popularity of distance learning in academic circles and society. The goal of this paper is to improve online music learning through the use of cloud computing and fuzzy clustering. A significant amount of data regarding students and their activities is collected. Following data cleaning and organization, students are grouped according to their preferred style of music learning using a technique called fuzzy C-mean (fcm) clustering. Each group is then given a customized learning plan that matches them with appropriate music resources. To put this into practice, a simple online music learning platform is created, enabling teachers and students to sign in, give instructions, view results, and log out with ease. To ensure that our approach remains effective even under high user loads, we designed the system to function properly for up to 400 concurrent users. An educational resource database is used to find the best learning materials for students, and fcm helps them pick the right resources. To make everything more organized, a resource library is designed using a simple language called XML. This library has different categories for learning materials and follows international standards to make sure everything works together smoothly. The performance of the system is measured in terms of scalability (8/10 response time, 7/10 throughput, 9/10 resource utilization), usability (8/10 user satisfaction, 7/10 task completion time, 6/10 error rate), effectiveness (9/10 learning outcomes, 8/10 student engagement, 9/10 retention rates). It has a 99% uptime and no security incidents have been reported. This study shows that our method can improve online music education, making it more personalized and effective for each student. The

关键词： Fuzzy clustering algorithm Cloud computing technology Distance music education Resource library fcm algorithm

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Adaptive learning algorithm based price prediction model for auction lots-deep clustering based interval quoting

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2412-e2412页

作者： Ke, Da Fan, Xianhua Asif, Muhammad Huazhong Univ Sci & Technol Sch Management Wuhan Hubei Peoples R China China Univ Geosci Sch Econ & Management Wuhan Hubei Peoples R China Natl Text Univ Dept Comp Sci Faisalabad Punjab Pakistan

This article addresses the problem of interval pricing for auction items by constructing an auction item price prediction model based on an adaptive learning algorithm. Firstly, considering the confusing class characteristics of auction item prices, a dynamic interclass distance adaptive learning model is developed to identify confusing classes by calculating the differences in prediction values across multiple classifiers for target domain samples. The difference in the predicted values of the target domain samples on multiple classifiers is used to calculate the classification distance, distinguish the confusing classes, and make the similar samples in the target domain more clustered. Secondly, a deep clustering algorithm is constructed, which integrates the temporal characteristics and numerical differences of auction item prices, using DTW-K-medoids based dynamic time warping (DTW) and fuzzy C-means (fcm) algorithms for fine clustering. Finally, the KF-LSTM auction item interval price prediction model is constructed using long short-term memory (LSTM) and dual clustering. Experimental results show that the proposed KF-LSTM model significantly improves the prediction accuracy of auction item prices during fluctuation periods, with an average accuracy rate of 90.23% and an average MAPE of only 5.41%. Additionally, under confidence levels of 80%, 85%, and 90%, the KF-LSTM model achieves an interval coverage rate of over 85% for actual auction item prices, significantly enhancing the accuracy of auction item price predictions. This experiment demonstrates the stability and accuracy of the proposed model when applied to different sets of auction items, providing a valuable reference for research in the auction item price prediction field.

关键词： Adaptive learning algorithm Dual clustering LSTM fcm algorithm Interval price prediction

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The application of fcm-based computer image segmentation technology in agricultural production

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SERVICE ORIENTED COMPUTING AND APPLICATIONS 2024年 1-11页

作者： Liao, Heng Huang, Huadong Guangxi Int Business Vocat Coll Sch Intelligence & Informat Engn Nanning 530007 Guangxi Peoples R China

Traditional pest and disease detection methods mainly rely on manual observation and diagnosis, which is not only inefficient but also prone to errors. Therefore, studying an efficient and accurate computer image segmentation technology is of great significance for agricultural production. Therefore, this study employs the fuzzy C-means clustering algorithm and support vector machine to segment images in agricultural production through computer image segmentation technology, with the aim of solving issues in agricultural production. Based on the collected image dataset of the same crop with different diseases and degrees of disease, experiments are conducted. The results show that the average hue of the sample is between 0.0015 and 0.2613, and the average saturation is between 0.0136 and 0.6513. The histogram of pest and disease image segmentation in the research algorithm is relatively dense, indicating that the research algorithm has advantages in crop pest and disease image segmentation. Under different numbers of images, the regional consistency of different diseases is not significant, with an average consistency of over 8.0. Among the disease prediction accuracy, the highest is black spot disease, with a prediction accuracy of 96.3%. This indicates that the technology studied can effectively extract the characteristics of crop diseases and pests, improving the accuracy and efficiency of disease and pest detection. Meanwhile, this technology can also provide technical support for other applications in agricultural production, such as precision agriculture and intelligent agriculture.

关键词： Computer image segmentation technology fcm algorithm Support vector machine Agricultural production Pest and disease images

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A Novel Double-Layer Strategy for Three-Phase Imbalance Treatment Using an Improved Genetic algorithm Based on MIT-LXPM

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第6期59卷 7404-7416页

作者： Wang, Jing Liu, Kaipei Qin, Liang Fan, Di Wang, Hong Wang, Feng Huang, Fucheng Chen, Jianhua Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China State Grid Jibei Elect Power Co Ltd Elect Power Res Inst Beijing 100045 Peoples R China State Grid Jibei Elect Power Co Ltd Beijing 100032 Peoples R China

Three-phase imbalance in distribution networks poses significant risks to line loss, economic efficiency, and the safe operation of equipment. However, existing approaches often overlook users who cannot adjust their phase sequence in the distribution network and fail to consider the flexible regulation capabilities of household appliances. To address this gap, this article introduces a novel double-layer treatment strategy for mitigating three-phase imbalance. The first layer of the strategy focuses on coarse adjustments to the user phase sequence, while the second layer refines the activation time of electrical equipment. Given the complexity of the proposed strategy, traditional algorithms are prone to local optimization. To overcome this limitation, an improved genetic algorithm based on the MIT-LXPM framework is proposed to implement the double-layer treatment. Furthermore, we construct a typical electricity consumption behavior database based on the weighted fuzzy C-means (Wfcm) algorithm, which facilitates more accurate clustering and minimizes optimization errors. Experimental results demonstrate the effectiveness of the proposed method, achieving a significant reduction in the average three-phase imbalance degree from 36.25% to 2.76%. Moreover, the line loss reduction rate reaches 80.26%. These findings highlight the practical value and application potential of the proposed method in real-world scenarios.

关键词： fcm algorithm genetic algorithm smart household electrical equipment three-phase imbalance user phase sequence

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Cost-effective land cover classification for remote sensing images

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-12页

作者： Li, Dongwei Wang, Shuliang He, Qiang Yang, Yun Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic Australia

Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which are mainly generated from remote sensing image classification techniques. Traditional land cover classification usually needs tremendous computational resources, which often becomes a huge burden to the remote sensing community. Undoubtedly cloud computing is one of the best choices for land cover classification, however, if not managed properly, the computation cost on the cloud could be surprisingly high. Recently, cutting the unnecessary computation long tail has become a promising solution for saving cost in the cloud. For land cover classification, it is generally not necessary to achieve the best accuracy and 85% can be regarded as a reliable land cover classification. Therefore, in this paper, we propose a framework for cost-effective remote sensing classification. Given the desired accuracy, the clustering algorithm can stop early for cost-saving whilst achieving sufficient accuracy for land cover image classification. Experimental results show that achieving 85%-99.9% accuracy needs only 27.34%-60.83% of the total cloud computation cost for achieving a 100% accuracy. To put it into perspective, for the US land cover classification example, the proposed approach can save over $1,593,490.18 for the government in each single-use when the desired accuracy is 90%.

关键词： Remote sensing Land cover classification Cloud computing fcm algorithm

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A Method for Identifying Gross Errors in Dam Monitoring Data

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WATER 2024年第7期16卷 978页

作者： Chen, Liqiu Gu, Chongshi Zheng, Sen Wang, Yanbo Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210098 Peoples R China

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (fcm) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the fcm algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.

关键词： dam monitoring data gross errors environmental change fcm algorithm OPTICS algorithm LOF algorithm

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Determination of threshold of asphalt pavement structure crack damage rate to quantify the severity of structural crack damage

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INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING 2024年第1期25卷

作者： Zhang, Weiguang Chang, Haolei Lin, Jun Southeast Univ Sch Transportat Nanjing Peoples R China Jiangxi Prov Highway Engn Testing Ctr Nanchang Peoples R China

Cracks in asphalt pavement are typically identified by analyzing electromagnetic or mechanical wave propagation. Validation is done by examining core samples obtained through drilling. This study proposes a threshold based on the structure crack damage rate (SCDR) collected by ground-penetrating radar (GPR) to quantify the severity of structure cracks. The Fuzzy c-means (fcm) algorithm categorises SCDR into different severity levels. Results indicate clear separation among most data points, but some overlap exists, particularly between non-structural and low-structural damage, and low-structural and medium-structural damage. High-structural damage areas are consistently distinct. The most successful outcomes are achieved when dividing structural crack damage levels into three groups. Thresholds for crack damage rates are ultimately determined to be 0.1909% to differentiate between non-structural and low-structural damage, and 0.6632% to differentiate between low-structural and high-structural damage. These findings serve as a foundation for assessing asphalt pavement crack damage and guiding maintenance decisions.

关键词： Ground penetrating radar Asphalt pavement structure crack structural crack damage rate threshold fcm algorithm deflection

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A method for classifying citrus surface defects based on machine vision

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021年第3期15卷 2877-2888页

作者： Zhang, Wenzhuo Tan, Aijiao Zhou, Guoxiong Chen, Aibin Li, Mingxuan Chen, Xiao He, Mingfang Hu, Yahui Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha Peoples R China Cent South Univ Forestry & Technol Hunan Prov Key Lab Urban Forest Ecol Changsha Peoples R China Hunan Acad Agr Sci Inst Plant Protect Changsha Hunan Peoples R China

When detecting citrus surface defects, the performance of machine vision system is affected by different aspects such as the size, shape and environment. Therefore, a method for classifying citrus surface defects based on machine vision was proposed in this paper. First, the Fuzzy C-Means algorithm optimized by the Gray Wolf Optimizer algorithm was used to preprocess the citrus image. The citrus in the image was separated from the background;Then, the improved convolutional neural network combined with the State Transfer algorithm (STA) was used to identify the citrus surface defects. We selected 2000 Tribute Citrus, 1000 ones with and without the defects separately, to carry on the experiment. The identification accuracy of the trained model on the dataset was 99.1%. In order to verify the effectiveness of the model in complex background, the convolutional neural network in combination with a STA was compared with SVM, AlexNet, VGG16 and other methods. The experimental results show that the citrus surface defect classification method based on machine vision is effective.

关键词： Citrus surface defects Convolutional neural network Machine vision fcm algorithm GWO algorithm State Transition algorithm

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