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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Shuanglu Dai Hong Man Shu Zhan M.S. student of Electrical Engineering Stevens Institute of Technology 1 Castle Point on Hudson Hoboken NJ07030 U.S.A the Department of Electrical and Computer Engineering Stevens Institute of Technology 1 Castle Point on Hudson Hoboken NJ07030 U.S.A School of Computer and Information Hefei University of Technology 193Tunxi Rd. Hefei 230009 China

ISBN: (纸本)9781424492695

Accurate and accelerated MRI tissue recognition is a crucial preprocessing for real-time 3d tissue modeling and medical diagnosis. This paper proposed an information de-correlated clustering algorithm implemented by variational level set method for fast tissue segmentation. The key idea is to design a local correlation term between original image and piecewise constant into the variational framework. The minimized correlation will then lead to de-correlated piece-wise regions. Firstly, by introducing a continuous bounded variational domain describing the image, a probabilistic image restoration model is assumed to modify the distortion. Secondly, regional mutual information is introduced to measure the correlation between piecewise regions and original images. As a de-correlated description of the image, piecewise constants are finally solved by numerical approximation and level set evolution. The converged piecewise constants automatically clusters image domain into discriminative regions. The segmentation results show that our algorithm performs well in terms of time consuming, accuracy, convergence and clustering capability.

关键词： clustering algorithm Fast MRI Tissue Segmentation image clustering algorithms Level set images Imagery (Psychotherapy) original image Piecewise Image numerical approximation image restoration technique segmentation Segmentation algorithms

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Evaluation of Transformer Bushing Moisture Faults Based on clustering algorithms and TOPSIS 16

Evaluation of Transformer Bushing Moisture Faults Based on C...

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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Liu, Yiming Li, Changyun Hou, Qingtao Yan, Hongwei Cao, Xin Xu, Minling Shandong Univ Sci & Technol Coll Elect & Automat Engn Qingdao Shandong Peoples R China

ISBN: (纸本)9781665422482

With the continuous expansion of the scale of power grids, the amount of monitoring data of power equipment is growing and the reliability demand of power equipment is increasing. In order to cope with power transformer accidents caused by damp faults in oil-immersed bushings, this paper applies big data clustering technology to construct a bushing damp fault evaluation index system, and combines the posting progress obtained from TOPSIS method to achieve a quantitative assessment of the damp state of bushings. The effectiveness of this method is also verified with examples.

关键词： clustering algorithm TOPSIS bushing fault assessment

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A Novel Hybrid SBM clustering Method Based on Fuzzy Time Series

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IEEE ACCESS 2023年 11卷 60693-60708页

作者： Zhang, Ren-Long Liu, Xiao-Hong Guizhou Univ Sch Management Guiyang 550025 Peoples R China

With the development of machine learning algorithm and fuzzy theory, the fuzzy clustering algorithm based on time series has received more and more attention. Based on the time series theory and considering the correlation of data attributes, it proposes a novel multivariate fuzzy time series clustering method based on Slacks Based Measure (MFTS-SBM). Compared with traditional fuzzy clustering that it has the ability to deal with fuzziness and uncertainty, the proposed hybrid SBM clustering method employs with input and output items and considers the clustering results and the influencing factors of nonparametric frontier. Thus, it is important for data decision making because decision makers are interested in understanding the changes required to combine input variables in order to classify them into the desired clusters. The simulation experiment results of different samples are given to explain the use and effectiveness of the proposed hybrid SBM clustering method. Therefore, the hybrid method has strong theoretical significance and practical value.

关键词： Fuzzy time series SBM nonparametric frontier clustering algorithm

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An accurate measurement method of lamb wave phase velocity based on clustering algorithms

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MEASUREMENT 2022年 195卷

作者： Gao, Xiang Tian, Ye Jiao, Jingpin Gao, Jie Li, Chenxu Beijing Univ Technol Fac Mat & Mfg Beijing Peoples R China

Due to the large detection range and high sensitivity to damages, Lamb waves are widely used in the localization and quantification of structural damages in a plate structure. The dispersion curve measurement is significant in the applications of Lamb waves, especially in a material with unknown properties. In the study, a method was proposed to measure dispersion curves based on clustering. Compared with traditional methods, the proposed method could realize more accurate and reliable measurement results with less measured data. This method was experimentally verified with an isotropic aluminum plate and an anisotropic CFRP plate. The relative error between measured and real values in an aluminum plate was less than 1%. With this method, A0 mode phasevelocity dispersion curves in CFRP plate with various braiding angles were experimentally obtained. This method facilitated Lamb wave defect detection and material parameter inversion.

关键词： Lamb waves Phase-velocity dispersion evaluation clustering algorithm CFRP plate

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Knowledge Extraction From PV Power Generation With Deep Learning Autoencoder and clustering-Based algorithms

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IEEE ACCESS 2023年 11卷 69227-69240页

作者： Miraftabzadeh, Seyed Mahdi Longo, Michela Brenna, Morris Politecn Milan Dept Energy Milan Italy

The unpredictable nature of photovoltaic solar power generation, caused by changing weather conditions, creates challenges for grid operators as they work to balance supply and demand. As solar power continues to become a larger part of the energy mix, managing this intermittency will be increasingly important. This paper focuses on identifying daily photovoltaic power production patterns to gain new knowledge of the generation patterns throughout the year based on unsupervised learning algorithms. The proposed data-driven model aims to extract typical daily photovoltaic power generation patterns by transforming the high dimensional temporal features of the daily PV power output into a lower latent feature space, which is learned by a deep learning autoencoder. Subsequently, the Partitioning Around Medoids (PAM) clustering algorithm is employed to identify the six distinct dominant patterns. Finally, a new algorithm is proposed to reconstruct these patterns in their original subspace. The proposed model is applied to two distinct datasets for further analysis. The results indicate that four out of the identified patterns in both datasets exhibit high correlation (over 95%) and temporal trends. These patterns correspond to distinct weather conditions, such as entirely sunny, mostly sunny, cloudy, and negligible power generation days, which were observed approximately 61% of the analyzed period. These typical patterns can be expected to be observed in other locations as well. Identified PV power generation patterns can improve forecasting models, optimize energy management systems, and aid in implementing energy storage or demand response programs and scheduling efficiently.

关键词： & nbsp clustering algorithm data mining deep learning autoencoder pattern extraction and analysis PV power generation unsupervised learning

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Modeling and short-term prediction of HVAC system with a clustering algorithm

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ENERGY AND BUILDINGS 2014年第0期82卷 310-321页

作者： Tang, Fan Kusiak, Andrew Wei, Xiupeng Univ Iowa Seamans Ctr 3131 Dept Mech & Ind Engn Iowa City IA 52242 USA

Energy consumption and air quality index (AQI) prediction is important for efficient heating, ventilation, and air conditioning (HVAC) system operation and management. A data-mining approach is presented in this paper for modeling and short-term prediction of the complicated non-linear system. The multilayer perceptron (MLP) ensemble performs best among the data mining algorithms discussed in this paper. A clustering-based method from preprocessing input data to construct the prediction models is proposed to decreases the prediction errors and the computational cost. The effectiveness of the proposed method is validated through a practical case study with both modeling and short-term prediction. The analytical results showed that the method was capable of reducing the prediction errors for modeling and short-term prediction by 11.05% and 12.21%, respectively, comparing with the models built without clustering method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： HVAC Energy Air quality index Modeling Prediction clustering algorithm

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A Reliability Model of Wind Farm Considering the Complex Terrain and Cable Failure Based on clustering algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2014年第6期9卷 1891-1899页

作者： Liu, Wenxia Chen, Qi Zhang, Yuying Qiu, Guobing Lin, Chenghui North China Elect Power Univ Sch Elect & Elect Engn State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China Guizhou Elect Power Res Inst Guiyang Peoples R China

A reliability model of wind farm located in mountainous land with complex terrain, which considers the cable and wind turbine (WT) failures, is proposed in this paper. Simple wake effect has been developed to be applied to the wind farm in mountainous land. The component failures in the wind farm like the cable and WT failures which contribute to the wind farm power output (WFPO) and reliability is investigated. Combing the wind speed distribution and the characteristic of wind turbine power output (WTPO), Monte Carlo simulation (MCS) is used to obtain the WFPO. Based on clustering algorithm the multi-state model of a wind farm is proposed. The accuracy of the model is analyzed and then applied to IEEE-RTS 79 for adequacy assessment.

关键词： Cable failure clustering algorithm Complex terrain Reliability model Wind farm

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Methods on reflecting electricity consumption change characteristics and electricity consumption forecasting based on clustering algorithms and fuzzy matrices in buildings

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BUILDING SERVICES ENGINEERING RESEARCH & TECHNOLOGY 2022年第6期43卷 703-724页

作者： Zhao, Tianyi Zhang, Chengyu Ujeed, Terigele Ma, Liangdong Dalian Univ Technol Inst Bldg Energy Dalian Liaoning Peoples R China

The extraction and analysis of electricity consumption changing patterns are increasingly important, as they can guide in energy management and efficiency retrofitting. Consequently, it is necessary to extract the laws governing building electricity consumption characteristics. This method should be on an hour-scale and successfully applied online to various buildings. Under these conditions, the method should be as simple as possible to ensure excellent online applications. A matrix model method was developed based on the conventional K-nearest neighbour clustering algorithm, which introduced the concept of electricity intensity levels. This method used the slopes of the power consumption curve as the grading standard for the extraction and assessment of the electricity consumption laws. The validation results for seven different buildings with various functions and climate zones, including the mean absolute error, mean absolute percentage error, mean square error, root mean square error, and coefficient of variance, showed that this method met the aforementioned requirements. Moreover, this method can be used for power consumption prediction, which integrated a process of filtering historical data, leading to better accuracy and less data volume than that of other methods that use historical data for prediction.

关键词： Sustainable development of buildings electricity consumption changing patterns electricity consumption characteristics clustering algorithm energy consumption prediction

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A novel fuzzy hybrid quantum artificial immune clustering algorithm based on cloud model

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 35卷 1-13页

作者： Zhang, Ren-Long Shan, Mi-Yuan Liu, Xiao-Hong Zhang, Li-Hong Hunan Univ Coll Business Adm Changsha 410082 Hunan Peoples R China Liverpool John Moores Univ Liverpool Business Sch Liverpool L3 5UX Merseyside England

In this paper we propose a novel fuzzy hybrid quantum artificial immune clustering algorithm based on cloud model (C-FHQAI) to solve the stochastic problem. Fuzzy hybrid quantum artificial immune algorithm can be developed with some of the advantages of information processing where there is a certain amount of indeterminism with qubits, i.e. quantum bits, replacing classical neurons having deterministic states and also in place of the classical artificial immune algorithm with quantum operators. The fuzzy combinatorial fuzzy hybrid quantum artificial immune clustering algorithm (C-FHQAI) is more expressive than the other fuzzy theories and methods. Finally, numerical examples show that the clustering effectiveness of the C-FHQAI algorithm is fast convergence and improves the accuracy of the fuzzy calculation. We find that the C-FHQAI clustering algorithm has the perspective of widespread application. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： clustering algorithm C-FHQAI Cloud model Quantum computing

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Evaluation of wind farm aggregation using probabilistic clustering algorithms for power system stability assessment

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 30卷

作者： Rahman, MirToufikur Hasan, Kazi Sokolowski, Peter RMIT Univ Sch Engn Melbourne Australia

Wind farm integration in large-scale power systems for performing stability assessment requires significant modelling efforts and high computational time. In these cases, wind farm clustering is used to simplify the simulation efforts, but still, it requires a large number and composition of clusters to represent the abrupt change in wind speed and direction. A probabilistic clustering approach could be useful in such a case, which can identify the most probable cluster(s) in a wind regime within a timeframe, such as one year. This paper has presented a probabilistic clustering framework to represent the most recurring aggregated wind farm model throughout the whole year by implementing four clustering algorithms, namely (i) K-means, (ii) hierarchical, (c) fuzzy c-means, and (d) DBSCAN (density-based spatial clustering of applications with noise). The performance of the aggregated wind farm model has been compared with the detailed wind farm model in assessing the small-disturbance, frequency, and voltage stability of a power system. The simulation results show that the aggregated equivalent models (as identified by the probabilistic clustering approach) present the same level of accuracy while performing the simulation 10-times faster. This simulation efficiency could be very useful for performing dynamic studies for large-scale power systems.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： clustering algorithm Probabilistic aggregation Stability analysis Wind generation

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