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14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

作者： Boloorchi, Alireza T. Samadzadeh, M. H. Rahnavard, Nazanin Oklahoma State Univ Dept Comp Sci Stillwater OK 74078 USA Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA Oklahoma State Univ Elect & Comp Engn Dept Stillwater OK 74078 USA

ISBN: (纸本)9781479927845

In Wireless Sensor Networks (WSN), interactions of sensors to provide service to a specific task is a significant issue. Services provided by WSNs include collecting data from the environment and aggregating them to address queries, or processing the collected data and using the result to adjust environmental parameters such as temperature and moisture. In WSNs, a number of nodes may be simultaneously needed by some application, and these nodes usually need to interact with one another while running in parallel. It is desirable in WSNs that enough resources be assigned to the applications so that a typical application can acquire its needed resources as fast as possible. Distances among sensors assigned to an application typically constitute an important factor in saving communication energy. This paper concerns introducing a clustering algorithm to enhance the efficiency of resource assignment by reducing overall distances among cooperating sensors. In the proposed algorithm, clusters are formed with different sizes to assign just enough sensors to requesting application. Cluster sizes are determined based on an input file to the algorithm that contains the initial number of required clusters of each size. Several increasingly more inclusive versions of the proposed algorithm were studied that are reported in this paper. A performance analysis is provided that shows the proposed clustering algorithm outperforms the Kmeans clustering algorithm from different perspectives.

关键词： Wireless Sensor Networks clustering algorithm Clusterhead Kmeans Parallelism

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Exploring Energy-Balancing Adaptive clustering algorithm (EBACA) in Wireless Sensor Networks (WSN)

Exploring Energy-Balancing Adaptive Clustering Algorithm (EB...

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International Conference on Information Technology and Computer Application Engineering (ITCAE)

作者： Li, Zhiyu Northwestern Polytech Univ Sch Mech Xian 710072 Peoples R China

ISBN: (纸本)9781315813288;9781138000797

In the node-randomly-distributed clustering algorithms in WSN, it is widely accepted that the excessively centralized cluster nodes can result in unbalanced energy consumption. Section 3 of the full paper explains in some detail the results of our exploration on how to improve network load balancing with EBACA algorithm. The core of section 3 consists of (1) EBACA algorithm selects the cluster head with three parameters: node remaining energy, distance to the Sink node, node density in the cluster;(2) the communication among the cluster heads is optimized, in which selecting the path with shortest sum of Euclidean distance between the cluster heads to transmit data can lead to the least communication energy consumption. Simulation results show that EBACA algorithm, compared with Low Energy Adaptive clustering Hierarchy (LEACH) algorithm, can effectively realize the network energy balancing and prolong the network life.

关键词： Wireless Sensor Networks clustering algorithm Energy Balancing Network Life

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MetaDecoder: a novel method for clustering metagenomic contigs

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MICROBIOME 2022年第1期10卷 1-16页

作者： Cong-Cong Liu Shan-Shan Dong Jia-Bin Chen Wang, Chen Ning, Pan Guo, Yan Tie-Lin Yang Xi An Jiao Tong Univ Biomed Informat & Genom Ctr Sch Life Sci & Technol Key Lab Biomed Informat EngnMinist Educ Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Life Sci & Technol Key Lab Biomed Informat Engn Minist Educ Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Affiliated Hosp 2 Natl & Local Joint Engn Res Ctr Biodiag & Biother Xian 710004 Shaanxi Peoples R China

Background: clustering the metagenomic contigs into potential genomes is a key step to investigate the functional roles of microbial populations. Existing algorithms have achieved considerable success with simulated or real sequencing datasets. However, accurately classifying contigs from complex metagenomes is still a challenge. Results: We introduced a novel clustering algorithm, MetaDecoder, which can classify metagenomic contigs based on the frequencies of k-mers and coverages. MetaDecoder was built as a two-layer model with the first layer being a GPU-based modified Dirichlet process Gaussian mixture model (DPGMM), which controls the weight of each DPGMM cluster to avoid over-segmentation by dynamically dissolving contigs in small clusters and reassigning them to the remaining clusters. The second layer comprises a semi-supervised k-mer frequency probabilistic model and a modified Gaussian mixture model for modeling the coverage based on single copy marker genes. Benchmarks on simulated and real-world datasets demonstrated that MetaDecoder can be served as a promising approach for effectively clustering metagenomic contigs. Conclusions: In conclusion, we developed the GPU-based MetaDecoder for effectively clustering metagenomic contigs and reconstructing microbial communities from microbial data. Applying MetaDecoder on both simulated and real-world datasets demonstrated that it could generate more complete clusters with lower contamination. Using MetaDecoder, we identified novel high-quality genomes and expanded the existing catalog of bacterial genomes.

关键词： MetaDecoder clustering algorithm Metagenome DPGMM GMM

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Using Hybrid Wavelet Approach and Neural Network algorithm to Forecast Distribution Feeders

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第3期18卷 1587-1600页

作者： Bagheri, Mehdi Zadehbagheri, Mahmoud Kiani, Mohammad Javad Zamani, Iman Nejatian, Samad Islamic Azad Univ Dept Elect Engn Yasuj Branch Yasuj Iran Shahed Univ Elect & Elect Engn Dept Tehran Iran

In this paper, using an algorithm based on the combination of data based on neural network virology and bacterial nutrition algorithm, improves the performance of the neural network prediction method. Also, the selection of two types of downstream and upstream filters in the wavelet transformation increases the predictive efficacy of neurological prediction. Based on the results, the optimized clustered neural network method has a more favorable response than the other methods. By selecting the appropriate filter and multichannel processing method, the maximum error percentage has improved by 15%. However, compared to the neural network prediction method, the proposed method has more computational volume due to the use of wavelet transform and also three times the use of neural prediction. Due to the large number of layers and used neurons, the neural network method has a much higher computational volume than the linear prediction method, where the linear prediction method has a higher error than the proposed method depending on the data used for training.

关键词： Forecasting Neural network clustering algorithm Wavelet transforms Power distribution

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Adaptive ECMS Based on EF Optimization by Model Predictive Control for Plug-In Hybrid Electric Buses

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2023年第2期9卷 2153-2163页

作者： Sun, Xiaodong Xue, Mingzhou Cai, Yingfeng Tian, Xiang Jin, Zhijia Chen, Long Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China

The plug-in hybrid electric bus (PHEB) is a relatively common new energy bus for urban use. In this article, to achieve the purpose of adaptive real-time optimization for fuel economy, a novel energy management strategy (EMS) is proposed to predict the battery state of charge (SOC) and optimize the equivalent factor combined with an adaptive equivalent fuel consumption minimization strategy (A-ECMS). For the accuracy of prediction, $K$ -means and $K$ -nearest neighbors algorithm (KNN) are used to distinguish and classify paths. On this basis, the model predictive control algorithm is used to predict the vehicle battery SOC, with the selection of the equivalent factor optimized according to the battery reference SOC trajectory. The proposed strategy has been verified not only under various working conditions of the simulation software but also on the hardware-in-the-loop (HIL) platform. The experimental results show that the proposed strategy can better predict the change of SOC under mixed working conditions, and the predicted trajectory is close to the reference SOC trajectory. The simulation results and the HIL results both show that the proposed strategy performed well for fuel economy optimization.

关键词： clustering algorithm energy management strategy (EMS) model predictive control (MPC) plug-in hybrid electric bus (PHEB)

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A Design of Granular Classifier Based on Granular Data Descriptors

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第3期53卷 1790-1801页

作者： Zhu, Xiubin Wang, Dan Pedrycz, Witold Li, Zhiwu Xidian Univ Sch Electromech Engn Xian 710054 Peoples R China Univ Sci & Technol Coll Comp Sci & Technol Xian 710054 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia Macau Univ Sci & Technol Inst Syst Engn Taipa Macau Peoples R China

Designing effective and efficient classifiers is a challenging task given the facts that data may exhibit different geometric structures and complex intrarelationships may exist within data. As a fundamental component of granular computing, information granules play a key role in human cognition. Therefore, it is of great interest to develop classifiers based on information granules such that highly interpretable human-centric models with higher accuracy can be constructed. In this study, we elaborate on a novel design methodology of granular classifiers in which information granules play a fundamental role. First, information granules are formed on the basis of labeled patterns following the principle of justifiable granularity. The diversity of samples embraced by each information granule is quantified and controlled in terms of the entropy criterion. This design implies that the information granules constructed in this way form sound homogeneous descriptors characterizing the structure and the diversity of available experimental data. Next, granular classifiers are built in the presence of formed information granules. The classification result for any input instance is determined by summing the contents of the related information granules weighted by membership degrees. The experiments concerning both synthetic data and publicly available datasets demonstrate that the proposed models exhibit better prediction abilities than some commonly encountered classifiers (namely, linear regression, support vector machine, Naive Bayes, decision tree, and neural networks) and come with enhanced interpretability.

关键词： Granular computing clustering algorithms Data models Support vector machines Data analysis Task analysis Semantics Classification clustering algorithm granular computing (GrC) information granule principle of justifiable granularity

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An Effective clustering algorithm for Auto-Detecting Well-Separated Clusters 14

An Effective Clustering Algorithm for Auto-Detecting Well-Se...

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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： He, Jinyuan Zhao, Gansen Zhang, Hao Lan Ramamohanarao, Kotagiri Pang, Chaoyi Sun Yat Sen Univ Sch Software Engn Guangzhou Guangdong Peoples R China Zhejiang Univ NIT Ningbo Zhejiang Peoples R China South China Normal Univ Sch Comp Guangzhou Guangdong Peoples R China Zhejiang Univ Ctr SCDM NIT Ningbo Zhejiang Peoples R China Univ Melbourne Melbourne Vic Australia CSIRO Australian E Hlth Res Ctr Canberra ACT Australia

ISBN: (纸本)9781479942749

clustering is an important analysis method commonly used in many areas, including data mining, image processing, statistics, biology, and machine learning. In this paper, we introduce a novel effective clustering method based on Euclidean Distance called Self - Increaseclustering(SIC) for detecting well-separated clusters that can be either convex or non convex sets. Unlike most of the prevalent clustering algorithms, SIC does not require any initial parameters such as the number of clusters produced. Instead, SIC can discover the clusters number automatically based on the distribution of input data and separate these clusters effectively. In each iteration of SIC, a new cluster containing one randomly selected object is created and then this cluster increases by merging itself with the other objects or clusters near-by if certain criterion is satisfied. We evaluate SIC both from theoretical as well as practical points of view, and the experimental results show that SIC works effectively and efficiently on different data sets.

关键词： clustering algorithm Data mining Well-separated Convex Non-convex

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Research on unmanned transfer vehicle path planning for raw grain warehousing

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 6513-6533页

作者： Zhang, Boqiang Gao, Tianzhi Chen, Yanbin Jin, Xin Feng, Tianpei Chen, Xinming Henan Univ Technol Henan Key Lab Superhard Abras & Grinding Equipmen Zhengzhou Peoples R China Henan Univ Technol Sch Mech & Elect Engn Zhengzhou 450001 Peoples R China Zhengzhou Yutong Heavy Ind Co Ltd Zhengzhou Peoples R China

A large number of grain machinery and vehicle equipment are usually required in the raw grain storage phase, and these objects together form the path planning map environment for the unmanned grain transfer vehicle. After using LiDAR to build a map of the environment for path planning, these dense and cluttered obstacles tend to affect the path planning effect making the unmanned transfer vehicle create a crossing from the impenetrable dense obstacles. To address this problem, this paper firstly deals with obstacles by fusing the DBSCAN clustering algorithm and K-means clustering algorithm, clustering obstacles, and extracting the cluster centroid and boundary points of each obstacle class to avoid the above situation. Secondly, the specific A* algorithm is improved, the search field way of the A* algorithm is optimized, and the optimized 5x5 field search way is used instead of the traditional 3x3 field search way of A* to improve the node search efficiency of the algorithm. Finally, the repulsion function of the artificial potential field algorithm is added to the A* heuristic function as a safety function to increase the obstacle avoidance capability of the A* algorithm. After verification, the improvement can operate better in the dense and cluttered obstacle environment.

关键词： Grain depot food logistics clustering algorithm A* algorithm artificial potential field raster map

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Singer Identification using clustering algorithm 3

Singer Identification using Clustering Algorithm

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3rd International Conference on Communications and Signal Processing (ICCSP)

作者： Dharini, D. Revathy, A. Saranathan Coll Engn Dept ECE Panjappur 620012 Thiruchirappall India

ISBN: (纸本)9781479933587

The main objective of this paper is to discuss the effectiveness of features and clustering algorithm to evaluate the performance of the singer identification system. The goal of singer identification is to identify the singer independent of training data. The training and testing phase are done for direct film song (vocal with background) for 10 singers. In training phase 15 film songs of a singer is taken as input. The input songs are made to undergo a set of pre-processing steps. The three stages of preprocessing are pre-emphasis, frame blocking and windowing. The Perceptual Linear Prediction (PLP) features are extracted from each frames of pre-processed signal. The singer model is developed by K-means clustering algorithm for each singer. In clustering method, the cluster centroids are obtained for cluster size of 256 and stored. One model is created for each singer by performing training and testing on the songs considered directly. Mean of minimum distances is computed for each model. Singer is classified based on selection of the model which produces minimum of average. The singer information is main factor in organizing and exploring music data. Singer identification also extends its application in music indexing and retrieval.

关键词： PLP clustering algorithm Singer identification

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Raga Identification using clustering algorithm 3

Raga Identification using Clustering Algorithm

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3rd International Conference on Communications and Signal Processing (ICCSP)

作者： Sheba, L. Loretta Maria Paul Revathy, A. Saranathan Coll Engn Dept ECE Panjappur 620012 Thiruchirappall India

ISBN: (纸本)9781479933587

The main objective of this paper is to evaluate the performance of raga identification using clustering algorithm and to compare the accuracy of the raga identification system which uses Mel Frequency Cepstral Coefficients (MFCC) as feature and the system which uses pitch information along with MFCC features. The goal of Raga identification is to identify the raga independent of training data. The training and testing phase are done for direct film song (vocal with background music) for 10 ragas. In training phase 10 film songs for each raga based on singers is taken as input. The input songs are made to undergo frame blocking. The Mel Frequency Cepstral Coefficients (MFCC) are extracted for each frames of the input signal. Similarly training is also done with MFCC and Pitch features (by Cepstrum method). The raga model is developed by K-means clustering algorithm for each raga. In clustering method, the cluster centroids are obtained for cluster size of 256 and stored. One model is created for each raga. In the testing phase Minimum mean of distances is computed for each model. Raga is classified based on selection of the model which produces minimum of average. The main motive behind Raga identification is that it can be used as a good basis for music information retrieval of any Carnatic music songs or Film songs.

关键词： Raga Identification clustering algorithm MFCC and Pitch

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