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21st IEEE Mediterranean Electrotechnical Conference (IEEE MELECON)

作者： Sandeep, Jangam Rai, Shekha Natl Inst Technol Dept Elect Engn Rourkela India

ISBN: (数字)9781665442800

ISBN: (纸本)9781665442800

One of the most pressing priorities of smart grid adoption is monitoring and consistent recognition of power quality issues. Small signal oscillations are one of the major concerns in the present-day world due to the increase in the interconnection of multiple power sources. This paper proposes a clustering-based sequential k-mean algorithm (S-k-mean) to detect the number of low-frequency oscillatory modes in the given power system signal. It is shown that the suggested algorithm can reliably estimate the number of low-frequency modes present in a signal as it considers all the significant eigenvalues which represents the energy levels of dominant modes. The suggested algorithm works even when the signal is subjected to high noise levels. The robustness of the proposed algorithm over other techniques is validated through synthetic signals at various noise level. The efficacy of the proposed scheme is further shown using a two-area network simulated through MATLAB and real-time digital simulator (RTDS) and it is also tested with a real-time signal obtained from the Western Electricity Coordination Council (WECC).

关键词： k-mean algorithm Autocorrelation matrix (AC matrix) eigenvalues Phasor measurement unit (PMU)

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Brain MR Image Segmentation with Spatial Constrained k-mean algorithm and Dual-Tree Complex Wavelet Transform

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JOURNAL OF MEDICAL SYSTEMS 2014年第9期38卷 1-6页

作者： Zhang, Jingdan Jiang, Wuhan Wang, Ruichun Wang, Le Shenzhen Inst Informat Technol Dept Elect & Commun Shenzhen 518172 Peoples R China Shenzhen Inst Informat Technol Dept Basic Publ Course Shenzhen 518172 Peoples R China

In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with k-mean algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained k-mean algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art algorithms.

关键词： Medical image segmentation MR images Dual-tree complex wavelet transform k-mean algorithm

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k-mean and double cross-validation algorithm for LS-SVM in Sasang typology classification

K-mean and double cross-validation algorithm for LS-SVM in S...

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IEEE International Conference on Automation and Logistics

作者： Zhang, Qian Lee, ki Jung Whangbo, Taeg keun Kyungwon Univ Dept Comp Sci Songnam South Korea

ISBN: (纸本)9781424415304

The Sasang typology is the traditional typology theory in Oriental Medicine. The medical typology distributes people into four types based on their traits: Tae-Yang, So-Yang, Tae-Eum and So-Eum type. In the paper, we design a model for Sasang typology classification based on the least squares support vector machines (LS-SVM). We use k-mean algorithm for decision the feature index from the side face, then we add the side face ratios into our feature space for improvement. The choice of resample method is very important for parameters' optimization in SVM and it will influence the system's stability. We propose a novel resampling algorithm called double cross-validation through comparing the cross-validation with bootstrap approach. The result shows that the established model under double cross-validation algorithm based on LS-SVM is more robust with high performance and suffices for the requirements of control and optimization for classification processes.

关键词： double cross-validation least squares support vector machines bootstrap Sasang typology classification k-mean algorithm

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Short-term Electric Load Forecasting Using Data Mining Technique

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2012年第6期7卷 807-813页

作者： kim, Cheol-Hong koo, Bon-Gil Park, June Ho Pusan Natl Univ Dept Elect & Elect Engn Sch Elect Engn Pusan South Korea

In this paper, we introduce data mining techniques for short-term load forecasting (STLF). First, we use the k-mean algorithm to classify historical load data by season into four patterns. Second, we use the k-NN algorithm to divide the classified data into four patterns for Mondays, other weekdays, Saturdays, and Sundays. The classified data are used to develop a time series forecasting model. We then forecast the hourly load on weekdays and weekends, excluding special holidays. The historical load data are used as inputs for load forecasting. We compare our results with the kEPCO hourly record for 2008 and conclude that our approach is effective.

关键词： Data mining k-mean algorithm k-NN algorithm Short-term load forecasting Time series forecasting

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k-mean alignment for curve clustering

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2010年第5期54卷 1219-1233页

作者： Sangalli, Laura M. Secchi, Piercesare Vantini, Simone Vitelli, Valeria Politecn Milan MOX Dipartimento Matemat I-20133 Milan Italy

The problem of curve clustering when curves are misaligned is considered. A novel algorithm is described, which jointly clusters and aligns curves. The proposed procedure efficiently decouples amplitude and phase variability;in particular, it is able to detect amplitude clusters while simultaneously disclosing clustering structures in the phase, pointing out features that can neither be captured by simple curve clustering nor by simple curve alignment. The procedure is illustrated via simulation studies and applications to real data. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Functional data analysis Curve alignment Curve clustering k-mean algorithm

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Integration of Probability and Clustering Based Approaches in the Field of Black Spot Identification

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2019年第1期63卷 46-52页

作者： Ghadi, Maen Torok, Arpad Tanczos, katalin Budapest Univ Technol & Econ Fac Transport Engn & Vehicle Engn Dept Automot Technol POB 91 H-1521 Budapest Hungary Budapest Univ Technol & Econ Fac Transport Engn & Vehicle Engn Dept Transport Technol & Econ POB 91 H-1521 Budapest Hungary

The objective of the paper is to define a complex methodology to analyze black spot locations of road infrastructure network combining the benefit of both;Empirical Bayes method and k-mean clustering approach. In the first step, k-mean algorithm is used to define homogeneous accident clusters. The homogeneity is described in three terms: traffic conditions, geometric design of the road and accident characteristics. Then, Empirical Bayes method is applied to define black spots based on the determined clusters. Due to the combination of the introduced methods, a powerful technique is provided that is able to identify high-risk locations and cluster dependent segment length as the output of the model.

关键词： black spots empirical Bayesian k-mean algorithm cluster analysis road safety

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A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 73卷 111-125页

作者： Abualigah, Laith Mohammad khader, Ahamad Tajudin Hanandeh, Essam Said Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zarqa Univ Dept Comp Informat Syst POB 13132 Zarqa Jordan

krill herd (kH) algorithm is a novel swarm-based optimization algorithm that imitates krill herding behavior during the searching for foods. It has been successfully used in solving many complex optimization problems. The potency of this algorithm is very high because of its superior performance compared with other optimization algorithms. Hence, the applicability of this algorithm for text document clustering is investigated in this work. Text document clustering refers to the method of clustering an enormous amount of text documents into coherent and dense clusters, where documents in the same cluster are similar. In this paper, a combination of objective functions and hybrid kH algorithm, called, MHkHA, is proposed to solve the text document clustering problem. In this version, the initial solutions of the kH algorithm are inherited from the k-mean clustering algorithm and the clustering decision is based on two combined objective functions. Nine text standard datasets collected from the Laboratory of Computational Intelligence are used to evaluate the performance of the proposed algorithms. Five evaluation measures are employed, namely, accuracy, precision, recall, F-measure, and convergence behavior. The proposed versions of the kH algorithm are compared with other well-known clustering algorithms and other thirteen published algorithms in the literature. The MHkHA obtained the best results for all evaluation measures and datasets used among all the clustering algorithms tested.

关键词： Combination of objective functions Hybridization krill herd algorithm k-mean algorithm Text document clustering

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Enhancing the Swimmer Movement Techniques Using Cloud Computing and Artificial Intelligence

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MOBILE NETWORkS & APPLICATIONS 2024年第2期29卷 433-447页

作者： Liu, Xurui Zhang, Guobao Beijing Sport Univ China Swimming Coll Beijing Peoples R China

Nowadays, miniature sensors can communicate with intelligent tools and pervasive computing devices to analyze and assess sports and physical activities. These sensors allow us to collect data from various physical activities using the underlying framework from anywhere, at any time, and in any location. However, these devices generate a large volume of data. Thus, having as much data as possible to assess a sports person's physical activity is critical. Wearable devices with tiny sensors, edge and cloud computing, and artificial intelligence are the pillars that are capable to change the current level of analysing physical activities in sports. Based on these features, this paper aims to combine these three pillars of physical activities to enhance the athlete's profile by predicting their physique, and recommend special training. For this purpose, a novel framework is proposed that allows us to generate a dataset from the realistic ecological conditions. To ensure the efficiency of this work, we have conducted a comprehensive literature on sports and physical activities. We underlined the limitations of the data collection, sensors, and processing techniques from literature. We hypothesize that the acquisition of data, continuous measurement, and analysis of different processes will end up in a more reliable model with the help of edge and cloud computing devices that allow the data to stream without restriction. Personalized training and profile-type approaches are used for swimmer athletes in this paper. The experimental results show that the suggested integrated method provides significant data to coaches and players, enabling focused training, performance optimisation, and improved athlete healthcare.

关键词： Edge computation Cloud computing Artificial Intelligence k-mean algorithm Convolutional Neural Network (CNN)

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Machine Learning Based Blind Decoding for Space-Time Line Code (STLC) Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第5期68卷 5154-5158页

作者： Joung, Jingon Jung, Bang Chul Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea Chungnam Natl Univ Dept Elect Engn Daejeon 34134 South Korea

In this correspondence paper, a novel machine learning (clustering) based blind decoding method is proposed for the space-time line code (STLC) systems without the information of modulation size and channels. The number of clusters, which is equivalent to the modulation size, is estimated from the received signals by using a k-mean cluster validation metrics, such as silhouette score and Davies-Bouldin index, and the cluster indices are directly mapped to the transmitted binary information bits. To improve the clustering performance, received signal normalization and the initial centroids of the clusters are designed by exploiting the combined STLC signal structure. From the numerical results, it is verified that the proposed blind decoding method can achieve near-coherent decoding performance with either small-size modulation, low noise at the receiver, or a large number of transmit antennas.

关键词： Machine learning clustering k-mean algorithm blind decoding space-time line code

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MOMHR: A Dynamic Multi-hop Routing Protocol for WSN Using Heuristic Based Multi-objective Function

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WIRELESS PERSONAL COMMUNICATIONS 2020年第2期111卷 883-907页

作者： Vinodhini, R. Gomathy, C. SRM Inst Sci & Technol ECE Dept Chennai 603203 Tamil Nadu India

Wireless sensor networks (WSNs) are the networks which mainly focuses on the applications and are composed of considerable sensor nodes. The use of energy in a valuable way is considered as a feature for the design structure of WSNs. In the WSNs, the nodes power sources are limited. Moreover, because of this, there is a must for a different approach regarding the energy availability and this is mainly for long distance communication, for this multi-hop (MH) systems are chosen. Even though MH decreases the energy cost used by all node along the path, however, to obtain the best routing path among nodes is yet an interesting subject. In this article, we present a multi-objective multi-hop routing (MOMHR) protocol for optimal data routing to gain the network lifetime. In the first phase, the k-means algorithm is applied to split the nodes into k clusters. Next, the artificial bee colony optimisation algorithm is applied to obtain the best possible CH within each cluster then using a multi-objective functions finally the multi-hop routing protocol finds a multihop path with minimum communication cost from the node to the base station. Our proposed method is simulated in MATLAB platform and compared with two recent protocols such as low energy adaptive clustering hierarchy and energy efficient centroid-based routing protocol. The execution of the proposed MOMHR protocols using multi-objective function is evaluated using metrics such as energy efficiency and network lifetime.

关键词： Wireless sensor networks Multi-hop routing Ant bee colony optimisation k-mean algorithm Multi-objective function

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