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Unsupervised cluster-based method for segmenting biological tumour volume of laryngeal tumours in ¹⁸F-FDG-PET images

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IET IMAGE PROCESSING 2017年第6期11卷 389-396页

作者： Tafsast, Abdelghani Hadjili, Mohamed Laid Bouakaz, Ayache Benoudjit, Nabil Univ Batna 2 Lab Automat Avancee & Anal Syst Batna Algeria High Sch Comp Sci HEB ESI Ecole Super Informat Brussels Belgium Univ Francois Rabelais Tours UMR Inserm Imagerie & Cerveau U930 Tours France

In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (F-18-FDG-PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in F-18-FDG-PET images. The technique is based on the k-means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7%) compared with other existing methods in clinical routine, including fuzzy c-means with (35.58%), gradient-based method with (19.14%) and threshold-based methods.

关键词： pattern clustering image segmentation tumours positron emission tomography medical image processing unsupervised cluster-based method laryngeal tumour biological tumour volume segmentation F-18-FDG-PET image 18-fluorodeoxyglucose positron emission tomography radiotherapy biological tumour volume delineation BTV delineation k-means clustering algorithm automatic optimal cluster number estimation intrinsic positron emission tomography image information patient clinical dataset

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A New Discharge Pattern for the Characterization and Identification of Insulation Defects in GIS

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ENERGIES 2018年第4期11卷 971页

作者： Yao, Rui Hui, Meng Li, Jun Bai, Lin Wu, Qisheng Changan Univ Sch Elect & Control Engn Xian 710064 Shaanxi Peoples R China State Grid Shaanxi Elect Power Res Inst Xian 710049 Shaanxi Peoples R China

Identification of insulation defects in gas insulated metal-enclosed switchgear (GIS) is important for partial discharge (PD) evaluation. This article proposes a polar coordinate pattern approach to characterize the different kinds of defect types. These defect types include floating electrodes, a fixed protrusion on the enclosure, surface contamination on the spacer, metallic prominence on the high voltage electrode, a void in the insulator, and free metal particles on the enclosure. First, the physical models for the insulation defects in the established GIS model are designed. Second, the phase resolved pulse sequence (PRPS) data sets are obtained using ultra-high frequency (UHF) measurement. Then, the polar coordinate patterns are proposed to characterize the defects. Nine discharge parameters combined with the parameters based on quadrant statistical theory constitute the input feature vector to identify the PD types. The experimental results show that these new parameters could produce a clear, quantitative description of the characteristics of the defect types and could be used to distinguish between the different kinds of defect types.

关键词： gas insulated metal-enclosed switchgear (GIS) partial discharge polar coordinate pattern k-means clustering algorithm feature vector classification

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Polar Coordinate Pattern Approach to Partial Discharge on Floating Electrode Defect in GIS

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2017年第6期12卷 825-833页

作者： Yao, Rui Zhang, Yanbin Si, Gangquan Yuan, Yiwei Xie, Qian Xi An Jiao Tong Univ Sch Elect Engn State key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China

This paper proposes a polar coordinate pattern approach to characterize the floating electrode defect in gas-insulated metal-enclosed switchgear (GIS). A 220-kV GIS bus bar cavity is taken as an object to design the physical model of the floating electrode defect. The partial discharge (PD) development process is studied when the applied voltage is gradually increasing. The phase-resolved partial discharge (PRPD) patterns and the polar coordinate patterns at different voltage levels are accessed through phase-resolved pulse sequence (PRPS) data in the ultrahigh-frequency (UHF) measurement. The proposed polar coordinate pattern approach does not use the traditional positive half-cycle and negative half-cycle analysis method in the PRPD pattern. It uses the clustering results to analyze the characteristics of the PD. It can be concluded that there are several advantages to the polar coordinate pattern. First, in the polar coordinate pattern, the phase is in a head-to-tail arrangement, so the observation of the PD phase characteristics can be more intuitive. Second, the polar coordinate pattern makes the calculated phase median, amplitude median, and centroid meaningful. With the voltage increasing, the variation law of the phase median and centroid can be observed. Third, the discharge parameters, which are based on the quadrant and statistical theory and the cosine similarity proposed in this paper, are from a new point of view. They can make a clear description of the characteristics of the floating electrode defect quantitatively, which can be distinguished from other insulation faults. Additionally, the continuous mode and phase mode at different voltage levels measured by acoustic detection are obtained to further demonstrate the PD development process. The acoustic results agree with the UHF measurement results. The efficiency and rationale of the polar coordinate pattern are further verified. (C) 2017 Institute of Electrical Engineers of Japan. Published by Joh

关键词： gas-insulated switchgear (GIS) partial discharge floating electrode polar coordinate pattern k-means clustering algorithm

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A new approach for solution of vehicle routing problem with hard time window: an application in a supermarket chain

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2017年第12期42卷 2067-2080页

作者： Comert, Serap Ercan Yazgan, Harun Resit Sertvuran, Irem Sengul, Hanife Sakarya Univ Dept Ind Engn Sakarya Turkey

In this study, a vehicle routing problem with hard time windows (VRPHTW) that appears to meet demands of customers' service within time intervals in a supermarket chain is solved. In VRPHTW, to reach a solution by an exact method is quite difficult and sometimes impossible if number of constraints is large enough (i.e., NP-hard), and solution time of a VRPHTW grows exponentially. As the size of the problem grows, a near optimal solution can be found using a heuristic method. A hierarchical approach consisting of two stages as "cluster-first route-second" is proposed. In the first stage, customers are assigned to vehicles using three different clustering algorithms (i.e., k-means, k-medoids and DBSCAN). In the second stage, a VRPHTW is solved using a MILP. The main contribution of the article is that the proposed hierarchical approach enables us to deal with a large size real problem and to solve it in a short time using the exact method. Finally, the proposed approach is employed on a supermarket chain. An instance of the algorithm is demonstrated to illustrate the applicability of the proposed approach and the results obtained are highly favourable.

关键词： Vehicle routing problem with hard time windows k-means clustering algorithm k-medoids clustering algorithm DBSCAN clustering algorithm cluster first route second approach

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Single source bins detection-based localisation scheme for multiple speech sources

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ELECTRONICS LETTERS 2017年第6期53卷 430-432页

作者： Jia, Maoshen Sun, Jundai Deng, Feng Sun, Junyue Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Dolby Labs Beijing Peoples R China Dialog Semicond Tianjin Peoples R China

A single source bins (SSBs) detection based multiple source localisation scheme is proposed. This scheme is based on detecting the SSBs in mixture signals that are only derived from one source. Specifically, after proposing a DOA convergence' assumption, k-means clustering algorithm is used for SSBs detecting. Thus, the multiple source localisation is converted to a single source one among these SSBs. Moreover, the proposed SSBs detection is applicable to other localisation methods and not limited to specific microphone topology. Experimental results demonstrate the localisation accuracy of the proposed method outperforms the localisation approaches which are based on single source zone detection.

关键词： direction-of-arrival estimation speech processing pattern clustering single source bins detection-based multiple localisation scheme multiple speech sources SSB DOA convergence k-means clustering algorithm

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A Support Vector Machine Forecasting Model for Typhoon Flood Inundation Mapping and Early Flood Warning Systems

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WATER 2018年第12期10卷 1734页

作者： Chang, Ming-Jui Chang, Hsiang-kuan Chen, Yun-Chun Lin, Gwo-Fong Chen, Peng-An Lai, Jihn-Sung Tan, Yih-Chi Natl Taiwan Univ Res Ctr Climate Change & Sustainable Dev Taipei 10617 Taiwan Natl Taiwan Univ Dept Bioenvironm Syst Engn Taipei 10617 Taiwan Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan Natl Taiwan Univ Hydrotech Res Inst Taipei 10617 Taiwan Natl Taiwan Univ Ctr Weather Climate & Disaster Res Taipei 10617 Taiwan

Accurate real-time forecasts of inundation depth and extent during typhoon flooding are crucial to disaster emergency response. To manage disaster risk, the development of a flood inundation forecasting model has been recognized as essential. In this paper, a forecasting model by integrating a hydrodynamic model, k-means clustering algorithm and support vector machines (SVM) is proposed. The task of this study is divided into four parts. First, the SOBEk model is used in simulating inundation hydrodynamics. Second, the k-means clustering algorithm classifies flood inundation data and identifies the dominant clusters of flood gauging stations. Third, SVM yields water level forecasts with 1-3 h lead time. Finally, a spatial expansion module produces flood inundation maps, based on forecasted information from flood gauging stations and consideration of flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we present an application to the Yilan River basin, Taiwan. The forecasting results indicate that the simulated water level forecasts from the point forecasting module are in good agreement with the observed data, and the proposed model yields the accurate flood inundation maps for 1-3 h lead time. These results indicate that the proposed model accurately forecasts not only flood inundation depth but also inundation extent. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response.

关键词： early flood warning disaster risk k-means clustering algorithm support vector machine flood inundation forecasting flood inundation map

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EFFICIENT COMPUTATION OF OPTION PRICES AND GREEkS BY QUASI-MONTE CARLO METHOD WITH SMOOTHING AND DIMENSION REDUCTION

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2017年第2期39卷 B298-B322页

作者： Weng, Chengfeng Wang, Xiaoqun He, Zhijian Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China Sun Yat Sen Univ Lingnan Univ Coll Guangzhou 510275 Guangdong Peoples R China

Discontinuities and high dimensionality are common in the problems of pricing and hedging of derivative securities. Both factors have a tremendous impact on the accuracy of the quasi-Monte Carlo (QMC) method. An ideal approach to improve the QMC method is to transform the functions to make them smoother and having smaller effective dimension. This paper develops a two-step procedure to tackle the challenging problems of both the discontinuity and the high dimensionality concurrently. In the first step, we adopt the smoothing method to remove the discontinuities of the payoff function, improving the smoothness. In the second step, we propose a general dimension reduction method (called the CQR method) to reduce the effective dimension such that the better quality of QMC points in their initial dimensions can be fully exploited. The CQR method is based on the combination of the k-means clustering algorithm and the QR decomposition. The k-means clustering algorithm, a classical algorithm of machine learning, is used to find some representative linear structures inherent in the function, which are used to construct a matching function of the smoothed function. The matching function serves as an approximation of the smoothed function but has a simpler form, and it is used to find the required transformation. Extensive numerical experiments on option pricing and Greeks estimation demonstrate that the combination of the smoothing method and dimension reduction in QMC achieves substantially larger variance reduction even in high dimension than dealing with either discontinuities or high dimensionality single sidedly.

关键词： option pricing Greeks quasi-Monte Carlo smoothing dimension reduction k-means clustering algorithm

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Two-layer coordination architecture HIF detection with μPMU data

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JOURNAL OF ENGINEERING-JOE 2018年第15期2018卷 1033-1037页

作者： Wang, Xiaojun Zhang, Yongjie Luo, Yiping He, Jinghan Ling, Ping Fang, Chen Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China State Grid Shanghai Municipal Elect Power Co Elect Power Res Inst Shanghai 200437 Peoples R China

The detection of high-impedance fault (HIF) on distribution network has been one of the most difficult problems. This study presents a data-driven method for HIF detection by using single-ended micro-phasor measurement unit (mu PMU). This approach is based on the two-layer coordination architecture, local side with PMUs and master station for further analysis. At the local side, the PMUs gather the synchronous data and achieve feature extraction with k-means clustering algorithm and principal component analysis. For determining the amounts of data categories, the authors adopt a method based on silhouette coefficient. Meanwhile, send the characteristics to the master station, then detect the HIF fault through the support vector machine. Finally, the method was tested on a 34 nodes distribution network in PSCAD/EMTDC. The results justify the effectiveness and the proposed detection scheme has >85% accuracy.

关键词： pattern clustering support vector machines feature extraction principal component analysis phasor measurement fault diagnosis power distribution faults power engineering computing detection scheme two-layer coordination architecture HIF detection PMU data data-driven method master station synchronous data principal component analysis data categories HIF fault 34 nodes distribution network high-impedance fault detection single-ended microphasor measurement unit feature extraction k-means clustering algorithm silhouette coefficient support vector machine PSCAD-EMTDC

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Effective Multipath Mitigation Methods for RTk in Urban Environments

Effective Multipath Mitigation Methods for RTK in Urban Envi...

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第九届中国卫星导航学术年会

作者： kai-qin Lin Zhong-liang Deng Lu Yin Beijing University of Posts and Telecommunications

RTk is one of the most precise positioning technologies,which has been widely used in many ***,in an urban area,GNSS receiver easily receive multipath signals caused by signal reflection and diffraction by both line-of-sight(LOS) and non-line-of-sight(NLOS) *** signals degrade the phase observation quality and makes it difficult to detect integer ambiguities in RTk *** paper proposes two methods to mitigate multipath *** the first method,a least square equation is constructed by using the relationship between the classical satellite elevation angle noise model and the signal-to-noise ratio(SNR) noise *** least square residual is used to determine NLOS and LOS satellites based on the k-means clustering *** second method mitigates LOS multipath signal based on the consistency check between prediction residuals and prediction covariance of Extended kalman *** experiences show that the proposed tow methods can able mitigate multipath effect and increase the availability of integer ambiguity resolution.

关键词： RTk positionings multipath effect least square residual k-means clustering algorithm

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The Design and Implementation of Chinese-Uighur-English Online Dictionary Based on knowledge Graph 20

The Design and Implementation of Chinese-Uighur-English Onli...

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20th IEEE International Conference on Computational Science and Engineering (CSE) / 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC)

作者： Qiu, Lirong Yang, Huan Zhou, Rong Minzu Univ China Informat Technol Sch Beijing Peoples R China

ISBN: (纸本)9781538632215

Designing Chinese-Uighur-English online dictionary is very important for the development of ethnic scientific research and education, which is the basis for the work of Uighur semantic study. Online dictionary with a huge thesaurus can be implemented by knowledge graph, existing works have not addressed in much detail. In order to design the thesaurus of the online dictionary, this paper is based on the word-formation characteristics of Uighur vocabulary to create the Uighur knowledge graph, and proposes k-means clustering algorithm for the classification of Uighur vocabulary.

关键词： Chinese-Uighur-English online dictionary knowledge graph k-means clustering algorithm

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