检索结果-内蒙古大学图书馆

A preliminary study on wellbore flow interpretation of fiber optic vibration signals based on k-means clustering algorithm

引用

SN APPLIED SCIENCES 2022年第8期4卷 1-11页

作者： Wu, Xianzu Gan, Lixiong Yuan, Shixiong Rui, Deng Yangtze Univ Key Lab Oil & Gas Resources & Explorat Technol Minist Educ Wuhan 430100 Hubei Peoples R China

The wellbore flow analysis of optical fiber vibration signal depends on distributed optical fiber logging. Distributed optical fiber logging technology identifies the fluid in the well through distributed optical fiber acoustic sensor (DAS) and distributed optical fiber temperature sensor (DTS). Distributed optical fiber sensor has the advantages of small underground interference, high efficiency and low cost. In this paper, the wellhead data extracted by the distributed optical fiber acoustic sensor is used to calculate the upper bound of the fluid sound frequency band in the pipe by nonlinear least squares fitting. The k-means clustering algorithm is used to cluster the optical fiber vibration signals in the low frequency band. According to the clustering results, the ratio of the optical fiber signal eigenvalues of each production layers is obtained, and the trend of the ratio of the optical fiber signal eigenvalues of each production layers is judged to be close to the trend of the water absorption intensity. Compared with traditional acoustic logging, the wellbore flow analysis using distributed optical fiber acoustic sensor can quickly determine the production contribution of each layer and the change of fluid phase state in the production cycle. Combined with traditional production logging technology, distributed optical fiber logging shows its reliability and accuracy in data collection, logging interpretation and production application. Starting from the principle of distributed optical fiber acoustic sensing technology, this paper briefly expounds the properties of distributed optical fiber acoustic sensor and the principle of injection profile logging, systematically introduces the processing of distributed optical fiber acoustic data, and emphatically introduces the accuracy of k-means clustering algorithm for analyzing distributed optical fiber acoustic signal and qualitative judgment of production layer, which provides a new idea for judging the accura

关键词： Distributed fiber optic acoustic wave sensor Fiber optic vibration signal Nonlinear least squares fitting k-means clustering algorithm Accuracy

来源：评论

学校读者我要写书评

暂无评论

Application of k-means clustering algorithm to improve effectiveness of the results recommended by journal recommender system

引用

SCIENTOMETRICS 2022年第6期127卷 3237-3252页

作者： Vara, Narjes Mirzabeigi, Mahdieh Sotudeh, Hajar Fakhrahmad, Seyed Mostafa Shiraz Univ Dept Knowledge & Informat Sci Sch Educ & Psychol Shiraz Iran RICeST Dept Evaluat & Resource Dev Shiraz Iran Shiraz Univ Sch Educ & Psychol Dept Knowledge & Informat Sci Shiraz Iran Shiraz Univ Dept Comp Sci & Engn Sch Elect & Comp Engn Shiraz Iran

This study investigates to evaluate feasibility of k-means clustering algorithm in order to improve effectiveness of the results recommended by RICEST Journal Finder System. More than 15,000 papers published in filed of engineering journals during 2013-2017 were collected from their websites. Their titles, abstracts and keywords were extracted, normalized and processed in order to form the test body. According to the number of papers collected, using Cochran's formula, 400 papers completely relevant to the subject of each journal were randomly and proportionally selected and entered the system as queries in order to receive the journals recommended by the system before and after k-means clustering algorithm and the results were recorded. Finally, effectiveness of the system results was determined at each stage by leave-one-out cross validation method based on precision at k top ranked results. Also, opinions of subject reviewers on relevance of the target journal were investigated through a questionnaire. Results showed that before data clustering, only 40% of target journal was recommended at the first 3 ranks. But after k-means clustering algorithm, in more than 80% of searches, the target journal was retrieved at the first 3 ranks. Also, effectiveness of the recommendations, according to 210 subject reviewers, after k-means clustering algorithm, showed that more than 80% of the recommended journals are completely relevant to the given paper. According to the study results, data clustering can significantly increase effectiveness of the results recommended by journal recommender systems.

关键词： Journal recommender systems Effectiveness Scalability k-means clustering algorithm RICeST journal finder system

来源：评论

学校读者我要写书评

暂无评论

The new k-windows algorithm for improving the k-means clustering algorithm

引用

JOURNAL OF COMPLEXITY 2002年第1期18卷 375-391页

作者： Vrahatis, MN Boutsinas, B Alevizos, P Pavlides, G Univ Patras UPAIRC Dept Math GR-26500 Patras Greece UOP UPAIRC Dept Business Adm GR-26500 Patras Greece UOP UPAIRC Dept Comp Engn & Inf GR-26500 Patras Greece

The process of partitioning a large set of patterns into disjoint and homogeneous clusters is fundamental in knowledge acquisition. It is called clustering in the literature and it is applied in various fields including data mining, statistical data analysis, compression and vector quantization. The k-means is a very popular algorithm and one of the best for implementing the clustering process. The k-means has a time complexity that is dominated by the product of the number of patterns, the number of clusters, and the number of iterations. Also, it often converges to a local minimum. In this paper, we present an improvement of the k-means clustering algorithm, aiming at a better time complexity and partitioning accuracy. Our approach reduces the number of patterns that need to be examined for similarity, in each iteration, using a windowing technique. The latter is based on well known spatial data structures, namely the range tree, that allows fast range searches. (C) 2002 Elsevier Science (USA).

关键词： k-means clustering algorithm unsupervised teaming data mining range search

来源：评论

学校读者我要写书评

暂无评论

Content-based image retrieval using PSO and k-means clustering algorithm

引用

ARABIAN JOURNAL OF GEOSCIENCES 2015年第8期8卷 6211-6224页

作者： Younus, Zeyad Safaa Mohamad, Dzulkifli Saba, Tanzila Alkawaz, Mohammed Hazim Rehman, Amjad Al-Rodhaan, Mznah Al-Dhelaan, Abdullah Univ Teknol Malaysia Fac Comp Johor Baharu Malaysia Univ Mosul Fac Comp Sci & Math Mosul Iraq Prince Sultan Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Salman Bin Abdulaziz Univ MIS Dept CBA Alkharj Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia

In various application domains such as website, education, crime prevention, commerce, and biomedicine, the volume of digital data is increasing rapidly. The trouble appears when retrieving the data from the storage media because some of the existing methods compare the query image with all images in the database;as a result, the search space and computational complexity will increase, respectively. The content-based image retrieval (CBIR) methods aim to retrieve images accurately from large image databases similar to the query image based on the similarity between image features. In this study, a new hybrid method has been proposed for image clustering based on combining the particle swarm optimization (PSO) with k-means clustering algorithms. It is presented as a proposed CBIR method that uses the color and texture images as visual features to represent the images. The proposed method is based on four feature extractions for measuring the similarity, which are color histogram, color moment, co-occurrence matrices, and wavelet moment. The experimental results have indicated that the proposed system has a superior performance compared to the other system in terms of accuracy.

关键词： Content-based image retrieval CBIR k-means clustering algorithm Feature extraction Co-occurrence matrix Similarity index

来源：评论

学校读者我要写书评

暂无评论

Improved k-means clustering algorithm for exploring local protein sequence motifs representing common structural property

IEEE TRANSACTIONS ON NANOBIOSCIENCE

引用

IEEE TRANSACTIONS ON NANOBIOSCIENCE 2005年第3期4卷 255-265页

作者： Zhong, W Altun, G Harrison, R Tai, PC Pan, Y Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Georgia State Univ Dept Biol Atlanta GA 30303 USA

Information about local protein sequence motifs is very important to the analysis of biologically significant conserved regions of protein sequences. These conserved regions can potentially determine the diverse conformation and activities of proteins. In this work, recurring sequence motifs of proteins are explored with an improved k-means clustering algorithm on a new dataset. The structural similarity of these recurring sequence clusters to produce sequence motifs is studied in order to evaluate the relationship between sequence motifs and their structures. To the best of our knowledge, the dataset used by our research is the most updated dataset among similar studies for sequence motifs. A new greedy initialization method for the k-means algorithm is proposed to improve traditional k-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Our experiments indicate that the improved k-means algorithm satisfactorily increases the percentage of sequence segments belonging to clusters with high structural similarity. Careful comparison of sequence motifs obtained by the improved and traditional algorithms also suggests that the improved k-means clustering algorithm may discover some relatively weak and subtle sequence motifs, which are undetectable by the traditional k-means algorithms. Many biochemical tests reported in the literature show that these sequence motifs are biologically meaningful. Experimental results also indicate that the improved k-means algorithm generates more detailed sequence motifs representing common structures than previous research. Furthermore, these motifs are universally conserved sequence patterns across protein families, overcoming some weak points of other popular sequence motifs. The satisfactory result of the experiment suggests that this new k-means algorithm may be applied to other areas of bioinformatics resea

关键词： k-means clustering algorithm protein structure sequence motif

来源：评论

学校读者我要写书评

暂无评论

Channeling analysis of wavelet threshold processing based on k-means clustering algorithm

引用

ACTA GEOPHYSICA 2023年第5期71卷 2137-2147页

作者： Gan, Lixiong Li, Ming Cai, Wenyuan Li, Jian Chen, Zhanglong Sun, Jian Deng, Rui Yangtze Univ Key Lab Oil & Gas Resources & Explorat Technol Minist Educ Wuhan 430100 Hubei Peoples R China PetroChina Qinghai Oilfield Co Well Testing Co Mangya 817500 Qinghai Peoples R China CNPC Logging Co Ltd Xian 710077 Shaanxi Peoples R China

Through the spectrum noise logging technology, the oil field is dynamically monitored, and according to its simple logging instrument and convenient operation, the position of the outer channeling of the casing can be qualitatively judged by the abnormal noise of the measurement record, and the downhole production status of the water injection well can be accurately diagnosed. Fully grasp the problems of oil casing leakage, outer channeling and packer leakage in water injection wells, and enrich downhole operations. In this paper, the downhole noise signal data are standardized, and the k-means clustering algorithm is used to classify the downhole noise signal according to the correlation coefficient of different frequencies to obtain the low-frequency noise signal, and the low-frequency noise signal is clustered twice to obtain the channeling frequency band and the reservoir fluid frequency band. The accurate channeling frequency range is determined and conforms to the domestic and foreign research data. The channeling frequency band is processed by wavelet threshold, and the useless noise in the channeling frequency band is eliminated. The channeling noise signal curve after processing is analyzed, and the main output layers have an obvious amplitude back channeling. The k-means clustering algorithm is used to analyze the channeling frequency band, and the channeling noise is processed by wavelet threshold. It is a new noise signal curve processing method, which provides a new idea for the spectrum noise logging technology to master the problem of channeling outside the pipe in the water injection well.

关键词： Spectrum noise logging Channeling k-means clustering algorithm Wavelet threshold processing Accuracy

来源：评论

学校读者我要写书评

暂无评论

Mitigate the impact of transmitter finite extinction ratio using k-means clustering algorithm for 16QAM signal

引用

OPTICS COMMUNICATIONS 2018年 409卷 72-76页

作者： Yu, Miao Li, Yan Shu, Tong Zhang, Yifan Hong, Xiaobin Qiu, Jifang Zuo, Yong Guo, Hongxiang Li, Wei Wu, Jian Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing Peoples R China

A method of recognizing 16QAM signal based on k-means clustering algorithm is proposed to mitigate the impact of transmitter finite extinction ratio. There are pilot symbols with 0.39% overhead assigned to be regarded as initial centroids of k-means clustering algorithm. Simulation result in 10 GBaud 16QAM system shows that the proposed method obtains higher precision of identification compared with traditional decision method for finite ER and IQ mismatch. Specially, the proposed method improves the required OSNR by 5.5 dB, 4.5 dB, 4 dB and 3 dB at FEC limit with ER= 12 dB, 16 dB, 20 dB and 24 dB, respectively, and the acceptable bias error and IQ mismatch range is widened by 767% and 360% with ER = 16 dB, respectively. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Extinction ratio k-means clustering algorithm Quadrature amplitude modulation Coherent optical communication

来源：评论

学校读者我要写书评

暂无评论

Open cluster membership probability based on k-means clustering algorithm

引用

EXPERIMENTAL ASTRONOMY 2016年第1期42卷 49-59页

作者： Abd El Aziz, Mohamed Selim, I. M. Essam, A. Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Nahda Univ Fac Comp Sci Bani Suwayf Egypt Natl Res Inst Astron & Geophys Dept Astron Cairo Egypt HTI Cairo Egypt

In the field of galaxies images, the relative coordinate positions of each star with respect to all the other stars are adapted. Therefore the membership of star cluster will be adapted by two basic criterions, one for geometric membership and other for physical (photometric) membership. So in this paper, we presented a new method for the determination of open cluster membership based on k-means clustering algorithm. This algorithm allows us to efficiently discriminate the cluster membership from the field stars. To validate the method we applied it on NGC 188 and NGC 2266, membership stars in these clusters have been obtained. The color-magnitude diagram of the membership stars is significantly clearer and shows a well-defined main sequence and a red giant branch in NGC 188, which allows us to better constrain the cluster members and estimate their physical parameters. The membership probabilities have been calculated and compared to those obtained by the other methods. The results show that the k-means clustering algorithm can effectively select probable member stars in space without any assumption about the spatial distribution of stars in cluster or field. The similarity of our results is in a good agreement with results derived by previous works.

关键词： Galaxy Open clusters and associations Individual k-means clustering algorithm NGC 2266 NGC 188 Color-magnitude diagram (CMD)

来源：评论

学校读者我要写书评

暂无评论

Improving performance of classification on severity of ill effects (SEV) index on fish using k-means clustering algorithm with various distance metrics

引用

WATER PRACTICE AND TECHNOLOGY 2019年第1期14卷 101-117页

作者： Hamid, khakzad RusPers Grp Co Dept Engn Moscow 109263 Russia

The severity of ill effects (SEV) index is based on the limited meta-analysis of previous peer reviewed reports and consultations, and described as a function of duration of exposure to turbid conditions in fisheries or fish life stages by fish adapted to life in clear water ecosystems. In this study, the performance of classification by SEV index was investigated using the k-means clustering algorithm. This study is based on 303 tests undertaken on aquatic ecosystem quality over a wide range of sediment concentrations (1-50,000 mg SS/L) and durations of exposure (1-35,000 h). Training and testing data includes concentration of suspended sediment, duration of exposure, species and life stages as the input variables and the SEV index for fish as the output variable. Results indicate that the k-means clustering algorithm, as an efficient novel approach with an acceptable range of error, can be used successfully for improving the performance of classification by SEV index.

关键词： k-means clustering algorithm scale of the severity (SEV) sediment management turbidity currents

来源：评论

学校读者我要写书评

暂无评论

Dk-means: a deterministic k-means clustering algorithm for gene expression analysis

引用

PATTERN ANALYSIS AND APPLICATIONS 2019年第2期22卷 649-667页

作者： Jothi, R. Mohanty, Sraban kumar Ojha, Aparajita Pandit Deendayal Petr Univ Sch Technol Dept Comp Engn Gandhinagar India Indian Inst Informat Technol Design & Mfg Jabalpu Jabalpur Madhya Pradesh India

clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clustering algorithms have been devised for the same. k-means is one of the popular algorithms for gene data clustering due to its simplicity and computational efficiency. But, k-means algorithm is highly sensitive to the choice of initial cluster centers. Thus, the algorithm easily gets trapped with local optimum if the initial centers are chosen randomly. This paper proposes a deterministic initialization algorithm for k-means (Dk-means) by exploring a set of probable centers through a constrained bi-partitioning approach. The proposed algorithm is compared with classical k-means with random initialization and improved k-means variants such as k-means++ and MinMax algorithms. It is also compared with three deterministic initialization methods. Experimental analysis on gene expression datasets demonstrates that Dk-means achieves improved results in terms of faster and stable convergence, and better cluster quality as compared to other algorithms.

关键词： k-means clustering algorithm Initial cluster centers Gene expression clustering Microarray data analysis

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：