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

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 General 3D Space-Time-Frequency Non-Stationary Model for 6G Channels

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第1期20卷 535-548页

作者： Huang, Ziwei Cheng, Xiang Peking Univ Sch Elect Engn & Comp Sci Dept Elect State Key Lab Adv Opt Commun Syst & Networks Beijing 100871 Peoples R China

In this paper, a general three-dimensional (3D) space-time-frequency non-stationary model is proposed for sixth generation (6G) channels. From the proposed model, a novel method, so-called correlated cluster based birth-death (BD) process method, is developed to efficiently and jointly mimic the 3D channel space-time-frequency non-stationarity. In this developed method, the frequency non-stationarity is properly captured by correlated clusters, which are obtained via an unsupervised learning algorithm in machine learning, i.e., k-means clustering algorithm. Additionally, the developed method involves the cluster based space-time non-stationary modeling. Based on the correlation coefficient of clusters, the BD probabilities on the array and time axes are reasonably modified by the linear weight method and matrix iteration algorithm. Therefore, interactions among the space, time, and frequency non-stationary modeling are sufficiently considered. Important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the channel non-stationarity in space-time-frequency domains can be sufficiently characterized. Finally, the excellent agreement between the simulation results and measurements further verifies the accuracy of the proposed model.

关键词： 6G channel model 3D space-time-frequency non-stationarity correlated cluster based BD process method k-means clustering algorithm

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Efficient prediction of CU depth and PU mode for fast HEVC encoding using statistical analysis

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2016年 38卷 474-486页

作者： Liu, Zhaoyi Lin, Ting-Lan Chou, Chi-Chan Beijing Inst Technol Sch Informat & Elect 5 South Zhongguancun St Beijing 100081 Peoples R China Chung Yuan Christian Univ Dept Elect Engn 200 Zhongbei Rd Zhongli City 320 Taoyuan County Taiwan

High Efficiency Video Coding (HEVC) adopts complex quadtree-structured CU (coding unit) and PU (prediction unit) modes. Thus, the search process for optimal modes causes high computational complexity in HEVC. To solve this problem, a fast coding algorithm is proposed. Thirteen neighboring CTUs (Coding Tree Unit) are divided into three classes with the k-means method, and are selectively used as the reference CTUs to calculate the predicted CU depth levels for the current CTU. Additionally, for every CU depth, we skip some rarely used PU modes to reduce the complexity of the PU selection algorithm. Experimental results show that compared with the HEVC reference software, our algorithm can achieve 56.71% time saving with Low Delay Configuration profile and 59.76% with Random Access Configuration profile, whereas values of BDBR (Bjontegaard Delta Bit Rate) are only 1.0517% and 0.9918%, respectively. Compared with five state-of-the-art algorithms, the proposed algorithm has significant encoding time reduction with good bitrate performances. (C) 2016 Elsevier Inc. All rights reserved.

关键词： HEVC (High Efficiency Video Coding) CU (Coding Unit) size decision PU (Prediction Unit) mode decision Statistical analysis k-means clustering algorithm

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Effective Big Data Retrieval Using Deep Learning Modified Neural Networks

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MOBILE NETWORkS & APPLICATIONS 2019年第1期24卷 282-294页

作者： Prasanth, T. Gunasekaran, M. Bannari Amman Inst Technol Dept Informat Technol Sathyamangalam 638401 Tamil Nadu India

In big data, the information retrieval (IR) embraces the discovery of documents from a compilation of dataset which are related to the user query. Usually, the data retrieval systems are used to scan the data. The existent approaches that employ customary IR are wearisome for big document collections. Recently, IR approaches were developed, albeit these are faster comparing to the traditional method but the quality of the document retrieved is less. To overcome such difficulties, here, effectual big data retrieval utilizing Deep Learning Modified Neural Networks (DLMNN) is proposed. Initially, the general pre-processing along with feature extraction steps are taken place. In preprocessing stage, unwanted data are removed and also used for changing the unstructured data in to structured data then in FE is done using frequency and entropy calculation of the given input data. Secondly, find the closed recurrent item dataset, after that find the weight of provided data using entropy measure and frequent item measure. In the 3rd step, the documents are clustered utilizing the k-means algorithm and then classified using DLMNN. The k-means algorithm is utilized to partition the collection of documents into several clusters then the DLMNN is used for classifying the documents into positive and negative classes. The proposed DLNN weight parameters are optimized utilizing the cuckoo search (CS) optimization algorithm. The last stage on the training process section is generating a training data-base. In the other part, the retrieval process is performed, in this section it pre-processes the user query and discovers the frequency item set then gets retrieval data. Finally, check the similarity assessment, if the information is found then it is visualized, otherwise the document is returned to the initial position. Experimental results contrasted with the previous MRT and IRI-RAS techniques concerning precision, recall, F-measure along with computation time. The proposed documen

关键词： Big data retrieval Closed frequent item set Deep learning modified neural networks (DLMNN) Cuckoo Search algorithm (CSA) k-means clustering algorithm Mutual refinement technique (MRT) Intelligent rule-based relevant information retrieval algorithm with semantics (IRI-RAS)

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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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Congestion management of power system with uncertain renewable resources and plug-in electrical vehicle

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第6期13卷 927-938页

作者： Prajapati, Vijaykumar k. Mahajan, Vasundhara Sardar Vallabhbhai Natl Inst Technol Dept Elect Engn Surat India

The study describes the quantifying impact of plug-in electrical vehicles (PEVs) and renewable energy sources (RES) for congestion management of power system. The proposed congestion management problem is formulated considering uncertainties of wind, solar, number of PEVs and load condition over a day. The uncertainty modelling of solar, wind and PEVs is presented using beta, Rayleigh and normal distribution functions, respectively. The PEVs uncertainty is dependent on its number and increases with escalation in number. The degree of uncertainties of RES is dependent on corresponding variable (wind and solar). These uncertainties result in large number of scenarios which increases the computational burden. The k-means clustering algorithm is applied to reduce the number of scenarios. The objective function is formulated to minimise generation cost, rescheduling cost and PEV cost for congestion management. This system is analysed by using Monte Carlo simulation. The proposed methodology relieves the congestion and reduces the generation cost, total power generation, total loss with increasing number of PEVs. The test result indicates that PEVs not only act as small storage unit but it also provides power during peak hours. The proposed approach is modelled in GAMS environment and implemented on modified IEEE 39-bus system.

关键词： power generation scheduling power system management Monte Carlo methods power generation economics renewable energy sources power markets electric vehicles normal distribution solar power stations wind power plants power system uncertain renewable resources plug-in electrical vehicle renewable energy sources RES congestion management problem uncertainty modelling normal distribution functions total power generation modified IEEE 39-bus system generation cost minimization PEV uncertainty beta function Rayleigh distribution functions k-means clustering algorithm rescheduling cost minimization PEV cost minimization Monte Carlo simulation GAMS environment

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An improved algorithm for pile damage localization based on complex continuous wavelet transform

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SMART STRUCTURES AND SYSTEMS 2021年第3期27卷 493-506页

作者： Liu, Jing-Liang Lin, Cheng-Xu Ye, Xi-Jun Zheng, Wen-Ting Luo, Yong-Peng Fujian Agr & Forestry Univ Coll Transportat & Civil Engn Fuzhou 350002 Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou 510006 Peoples R China Fujian Univ Technol Coll Civil Engn Fuzhou 350118 Peoples R China

Since the complex continuous wavelet transform (CCWT) based pile damage detection method is empirical and subjective, an improved algorithm for pile damage localization based on CCWT is proposed by introducing k-means clustering and fast Fourier transform (FFT). In this method, the k-means clustering algorithm is used to accurately calculate the time coordinates of two energy concentrating points caused by the incident and reflected waves, respectively. Meanwhile, FFT is employed to estimate the concerned frequency band of the response signal. Therefore, a specific region in the time frequency plane is defined objectively and it can be used to search the phase angle turning points and localize pile damage. The proposed method is verified by numerical examples of piles with single and multiple damage positions. A parameter analysis is also conducted to investigate how damage depth and damage degree in piles affect the accuracy and effectiveness of the proposed method. The results demonstrate that the proposed method is able to localize a pile with a damage at least 2.5 m away from the pile head when the damage degree is as less as 5%. After that, dynamic tests of an actual square reinforced concrete pile and an actual circular reinforced concrete pile are investigated to verify the application of the proposed method on practical engineering. Although the proposed method is capable of localizing actual piles more accurately than the CCWT method, the problem of interference points needs to be addressed by mutual verification with other pile damage localization methods.

关键词： complex continuous wavelet transform k-means clustering algorithm fast Fourier transform phase angle damage localization

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作者： Veni, Rushikesh University of Nevada Las Vegas

学位级别：M.S.C.S.

Document clustering or unsupervised document classification is an automated process of grouping documents with similar content. A typical technique uses a similarity function to compare documents. In the literature, many similarity functions such as dot product or cosine measures are proposed for the comparison operator. For the thesis, we evaluate the effects a similarity function may have on clustering. We start by representing a document and a query, both as a vector of high-dimensional space corresponding to the keywords followed by using an appropriate distance measure in k-means to compute similarity between the document vector and the query vector to form clusters. Based on these clusters we decide the best distance metric for the document set used. Next, we compute time complexities for different similarity functions for the same model and document set based on the number of iterations and number of clusters.

关键词： Canberra distances Chi-Square Data mining Distances Document clustering Euclidean distances Execution time Information retrieval k-means clustering algorithm Similarity functions

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Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment

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JOURNAL OF kING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第6期34卷 3506-3516页

作者： khan, Saiqa Narvekar, Meera DJ Sanghvi Coll Engn Mumbai India

A large number of countries depend on agriculture. However, the biggest challenge for farmers is the plant-related diseases. The most observed part of the plant is leaf but the segmentation of a diseased lesion leaf image suffers from uneven illumination and cluttered complex natural environment problems. To overcome these problems, the proposed methodology is based on the fusion of color balancing and superpixel. First, the input image transforms into a color-balanced image to eliminate the effects of uneven illumination. Second, compact regions are created from the transformed image using superpixel operation and the empirically derived threshold is applied on Histogram of Gradients (HOG) and color channels of superpixel to separate the unwanted background from leaf image. k-means clustering is applied to find a diseased infected image. An extended form of HOG, Pyramid of HOG(PHOG) with Gray Level Co-occurrence Matrix (GLCM) features are used to represent a diseased infected part. Finally, different classifiers are compared and Random Forest (RF) is selected to classify diseases. To assess the robustness of the proposed method, an experiment is conducted to test its accuracy effectively on natural images acquired from various farms, downloaded images from the Internet and Plant-Village color dataset. Results indicate that the proposed method achieved an accuracy of 93.12% on a combined dataset using cross-validation to show that method is effective to classify diseases in the presence of a cluttered background. The proposed system is compared with ALDD method (Dhaygude and kumbhar, 2013), MVBPDR (Habib et al., 2018), AIMSD (Araujo and Peixoto, 2019) and CCF (Ma et al., 2017). The novelty of the proposed system lies in the fact that the proposed segmentation is effective for all the varied datasets and it is evaluated by True Positive Rate (TPR), False Positive Rate (FPR), False Negative Rate (FNR), Positive Prediction Value (PPV), False Discovery Rate (FDR), Accur

关键词： Tomato disease detection Color balancing Superpixel clustering k-means clustering algorithm Gray Level Co-occurrence Matrix (GLCM) Pyramid of histograms of orientation gradients (PHOG)

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Custom optical imaging system for ex-vivo breast cancer detection based on spectral signature

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SURGICAL ONCOLOGY-OXFORD 2020年 35卷 547-555页

作者： Aref, Mohamed Hisham Aboughaleb, Ibrahim H. El-Sharkawy, Yasser H. Mil Tech Coll Biomed Engn Dept El Fangary St Cairo Egypt

Background and purpose: Breast cancer is a popular well-known tumor in women globally and the subsequent driving reason for malignancy death. The purpose of the present study is to develop Low cost, commercial, and affordable system that discriminates malignant from normal breast tissues by exploiting the unique properties of Hyperspectral (HS) Imaging. Materials and methods: The difference in the optical properties of the investigated breast tissues gives various reactions to light transmission, absorption, and especially the reflection over the spectral range. A custom optical imaging system (COIS) was designed to assess variable responses to monochromatic LEDs (415, 565, 660 nm) to highlight the differences in the reflectance properties of malignant/normal tissue. Statistical analysis was computed for determining the ideal wavelength to differentiate between normal and malignant regions. The experiment was repeated using the same LEDs, and low-cost CCD camera to examine the capability of such a system to discriminate between normal and malignant tissue. Results: Spectral images obtained by Hyperspectral camera, have been analyzed to reveal the difference of reflectance malignant and normal breast tissue. Superficial spectral reflection image with blue LED (415 nm) showed high variance (10.11). However, a more-depth reflection image with red LED (660 nm) showed low variance (4.44). So the optimum contrast image was produced by combining the three spectral information images from blue, green, and red LED. The COIS using a commercial CCD camera was in agreement with the HS camera. Conclusions: The novel COIS of the commercial Low-cost CCD Camera is reliable and can be used with endoscopy technique as an assistant tool for surgical doctor to make decision and assess the resection edges in real time during surgery.

关键词： Breast cancer Hyperspectral imaging Low-cost CCD camera Monochromatic LED k-means clustering algorithm Spectral signature

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