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检索条件"主题词=K-Means clustering algorithm"

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Prenatal Detection of Ventricular Septal Defects by VGG -16 Model Using Ultrasound Images 3

Prenatal Detection of Ventricular Septal Defects by VGG -16 ...

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： kavitha, D. Lakshmi, C.R. CMR Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru India Cambridge Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350317022

Congenital heart defect is one of the important anomaly. The deformed heart can be differentiated from the normal heart based on several parameters such as the size of auricles, ventricles, valve and position of heart, area, circumference, and perimeter. One of the methods to detect the anomalies in fetal heart is by applying advanced image pprocessing techniques and artificial intelligence algorithms. This proposed system is the primary framework for diagnosing the prenatal ventricular septal defects (PVSD). The first step is to denoise the US images using enhanced anisotropic diffusion Enhanced Perona Malik Filter (EPMF), followed by k-means clustering segmentation method as the second step, and finally, VGG-16 architecture was implemented with the pre-trained weights from the database. The original image is compared with the reference image in terms of different parameters using a VGG16 deep learning algorithm to predict PVSD anomalies at the early stage of pregnancy. The experimental results shows that PSNR reach to high value of 33.45, Dice coefficient and jacquard index and conformity coefficient as 0.88-0.86. VGG-16 is the first attempt to diagnose prenatal ventricular septal defects to achieve an accuracy of 90%. This proposed system will give a second opinion for the doctors in diagnosing the abnormalities at the early stages. © 2023 IEEE.

关键词： Enhanced Perona Malik filter k-means clustering algorithm VGG-16 CNN model

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Real-time fault detection approach of software under big data environment

Real-time fault detection approach of software under big dat...

引用

2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Jian xianrui Chengdu vocational and technical college

ISBN: (纸本)9781510803084

For large data environment, the traditional k-means clustering algorithm flaw in the software in real-time fault detection process, we propose a k-means clustering software intelligent real-time fault detection method is improved. The k-means clustering algorithm and particle swarm optimization combined during the iterative process, the combination of k-means to optimize the upcoming PSO algorithm offspring individuals use k-means clustering to obtain local optimal solution calculates and uses these individuals continue to participate in an iterative process, so that the algorithm can improve the convergence speed, avoid falling into local optimal solution, to obtain accurate software fault signal characteristics. Experimental results show that the use of kmeans tilt feature extraction software, intelligent real-time fault detection algorithm can effectively improve the accuracy of fault detection, and achieved satisfactory results. On the condition of big data, traditional k-mean cluster algorithm showed some flaws of software fault detection in realtime way. In this research, an improved k-mean cluster method was proposed for the purpose of testing software fault in intellectual and real-time way. This improved method combined k-mean cluster algorithm with Particle cluster algorithm. k-mean cluster were used in the optimizing procedure among the iterative process. Individuals from Particle cluster algorithm were computed by using k-mean algorithm and obtained the locally optimal solution, and then these individuals continue to participate in the iterative processing. This improved converging rate of the algorithm, avoided falling into the locally optimal solution and finally got the accurate features of software fault signal. Results showed that this testing method with using the tilt feature of k-mean algorithm improved detection accuracy effectively.

关键词： k-means clustering algorithm feature extraction real-time software fault detection

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Research on intrusion detection based on feature extraction of autoencoder and the improved k-means algorithm

Research on intrusion detection based on feature extraction ...

引用

作者： Wang, Xingang Wang, Linlin School of Information Qilu University of Technology Jinan China

ISBN: (纸本)9781538636749

Nowadays, intrusion detection is a technology to effectively avoid a number of risks of network intrusion. The k-means algorithm is widely used in intrusion detection. But, the algorithm has some shortcomings, such as random selection of k value, sensitive selection of initial cluster centers, and low accuracy in clustering high-dimensional data. In order to make up for these shortcomings of the k-means algorithm, this paper proposes an AE-kmeans architecture which combines an autoencoder with the improved k-means algorithm. The AE-kmeans architecture realizes the dimension reduction and feature extraction of these original data by introducing an autoencoder, and uses the improved k-means algorithm to cluster these processed data. These improvements of the improved k-means algorithm mainly include two aspects: the first aspect, a new method is introduced to select initial cluster centers;the second aspect, the algorithm calculates the weight of each attribute with the coefficient of variation, and then uses these weights in Euclidean distance formula, resulting in a weighted Euclidean distance formula. Finally, the AE-kmeans architecture uses kDD CUP99 data set for intrusion detection simulation experiments. The Experimental results show that the AE-kmeans architecture not only enhances the ability to deal with high-dimensional data, but also improves the detection rate and reduces the error-detection rate compared with k-means algorithm. © 2017 IEEE.

关键词： AE-kmeans architecture Autoencoder Feature extraction Intrusion detection k-means clustering algorithm Weighted Euclidean distance formula

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Error Prediction method of Electronic Transformers Based on RBF Neural Network 13

Error Prediction method of Electronic Transformers Based on ...

引用

2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Wu Qiao Xu Xin Yang Shihai Lu Shufeng Chen Gang Electric Power Research Institute Jiangsu Electric Power Company School of Electrical Engineering Southeast University

ISBN: (纸本)9781509050369;9781509050352

With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry’s attention. In order to ensure the accuracy and reliability of electronic transformers, it is necessary to determine the error of the electronic transformers accurately through the study of the influence factors of the measurement error of the electronic transformers. In this regard, this paper proposes a transformer error prediction method which combines RBF neural network prediction with k-means clustering algorithm. First, the historical data are classified by k-means clustering. Then, train the prediction model with the historical data clustered. Take the influencing factors such as temperature, load capacity and magnetic field as the independent variables and the error as the prediction target to establish the prediction model. The prediction model is suitable for the analysis of a large number of data and can accurately predict the error. After using this model to analyze the long-term experimental data of Dongshan substation in 2016, the relative error of the error result is around 5%, which is relatively and verifies the validity of the model.

关键词： Electronic transformers k-means clustering algorithm RBF neural network long-term experimental data

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Research on ASIC for multi-speaker isolated word recognition

Research on ASIC for multi-speaker isolated word recognition

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2nd International Conference on ASIC

作者： Xiong, B Sun, YH Institute of Microelectronics Tsinghua University Beijing China

ISBN: (纸本)7543909405

The ASIC for multi-speaker speech recognition is design in this paper. The LPC-derived cepstral coefficients are chosen as speech features. Templates are trained by k-means clustering algorithm. Two stage recognition system can not only improve recognition accuracy, but also reduce the delay. The first stage of recognition system uses speech spectrum difference(SSD) algorithm. The second stage uses DTW. The whole recognition system is design into ASIC on high level with VHDL and simulated in Powerview.

关键词： speech recognition linear predictive coding application specific integrated circuits cepstral analysis ASIC multi-speaker isolated word recognition LPC cepstral coefficients k-means clustering algorithm template training speech spectrum difference algorithm DTW high level design VHDL Powerview simulation

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A Cluster Analysis of Power Load Forecasting Based on Power Consumption Data and Meteorological Data 4

A Cluster Analysis of Power Load Forecasting Based on Power ...

引用

4th International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2024

作者： Wei, Yi Tang, Zhong-Sheng College of Intelligent Networking and New Energy Vehicles Geely University of China Sichuan Province Chengdu City China

ISBN: (纸本)9798350378542

With the increasing of the big data volume for power consumption data and the complexity of meteorological data, it has become more difficult to cope with the power load forecasting in the recent energy crisis. To address this issue, we analyze the power consumption data and meteorological data in this paper, which would be applied in developing a more effective power load management for the intelligent power systems. We develop a hybrid day-ahead load forecasting models based on k-means clustering algorithm well-known neural networks Long Short-Term Memory (LSTM)). To study the impact of meteorological factors on power load forecasting, a cluster analysis is also conducted in this work to classify temperature data and rainfall data, which aims to mine the relationship between power consumption data and meteorological data. In the experiments on the public data sets, the performance evaluation of the proposed method is compared with the commonly used algorithms for power load forecasting. In the numerical results, it is shown that the proposed method performs higher accuracy and applicability compared with the traditional methods. © 2024 IEEE.

关键词： k-means clustering algorithm load forecasting Neural network Smart grid

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Complexity Analysis of Lumped Parameter Models: Development of Complexity Reduction algorithm

Complexity Analysis of Lumped Parameter Models: Development ...

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作者： YUXI LI Reykjavik University

学位级别：硕士

Lumped parameter models have been shown to be a useful tool for geothermal reservoir analysis and production planning. Tank models are a common form of lumped parameter models, incorporating tanks of given capacitance partially filled with fluid. Between the tanks are connections with given conductance, that allow fluid to flow between connected tanks with different fluid levels. In this thesis, we analyze how tank models of varying complexity compare in terms of accuracy and utility. An algorithm called Complexity Reduction algorithm (CRA) is developed that automatically finds those models that are likely to be the best by choosing a certain path through the model space. Since in general, it is reasonable to expect that a complex model is able to give an accurate fitting result and the optimum model indicated by CRA only has a medium complexity, a switch-back method is developed to decrease the training error of the complex models. Also, in some cases, there is a large number of production wells that are producing hot water, which will lead to a situation where very many parameters needed to be estimated, since the number of parameters grows quadratically in terms of the number of tanks. The k-means clustering algorithm is shown to be suitable for finding an initial production tank configuration under such situations. Real data from the Laugarnes geothermal field and Reykir geothermal area in Iceland is shown in the thesis. The results show that the newly developed algorithm provides insights into model selection for lumped parameter models. The accuracy of both history-matched and predicted drawdown for lumped parameter models of varying complexity and the results by using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as an indicator for model selection have been shown.

关键词： Geothermal reservoir modeling Lumped-parameter models Complexity analysis Information criterion Complexity Reduction algorithm Switch-back method Model selection k-means clustering algorithm

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Software Technology Immersive Education Research and Practice Based on Spark Platform 3

Software Technology Immersive Education Research and Practic...

引用

3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Genrang, Zheng Chunping, Huang Zhongshan Polytechnic Department of Information Engineering Guangdong Zhongshan China

ISBN: (纸本)9798350380989

From the perspective of education informatization, a teaching resource sharing system with huge storage space and computation amount is established. When the user accesses the teaching materials, it constructs the database with a large number of users through instant access to the data. This project intends to study an optimization scheme that does not require any intermediary output based on Hadoop and combined with Spark's parallel computing mode, so that it can make better use of Spark's iterative computing capability. It realizes the efficient recommendation of teaching resources. A hierarchical resource model based on collaborative filtering is proposed. The unrated resources predicted in this way are imprecise. A time function is added to the prediction process for non-rated resources to reflect the change in user interest over time. The index function is used to describe the change of time. The practicability of the Spark-based in the recommendation of teaching resources are verified by the research. © 2024 IEEE.

关键词： collaborative filtering Educational resources recommendation k-means clustering algorithm recommendation system spark

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Error Prediction method of Electronic Transformers Based on RBF Neural Network

Error Prediction method of Electronic Transformers Based on ...

引用

13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Wu Qiao Xu Xin Yang Shihai Lu Shufeng Chen Gang Jiangsu Elect Power Co Elect Power Res Inst Nanjing 211103 Jiangsu Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781509050352

With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry's attention. In order to ensure the accuracy and reliability of electronic transformers, it is necessary to determine the error of the electronic transformers accurately through the study of the influence factors of the measurement error of the electronic transformers. In this regard, this paper proposes a transformer error prediction method which combines RBF neural network prediction with k-means clustering algorithm. First, the historical data are classified by k-means clustering. Then, train the prediction model with the historical data clustered. Take the influencing factors such as temperature, load capacity and magnetic field as the independent variables and the error as the prediction target to establish the prediction model. The prediction model is suitable for the analysis of a large number of data and can accurately predict the error. After using this model to analyze the long-term experimental data of Dongshan substation in 2016, the relative error of the error result is around 5%, which is relatively and verifies the validity of the model.

关键词： Electronic transformers k-means clustering algorithm RBF neural network long-term experimental data

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Research on Intrusion Detection Based on Feature Extraction of Autoencoder and the Improved k-means algorithm

Research on Intrusion Detection Based on Feature Extraction ...

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Wang, Xingang Wang, Linlin Qilu Univ Technol Sch Informat Jinan Shandong Peoples R China

ISBN: (纸本)9781538636749

关键词： intrusion detection k-means clustering algorithm autoencoder AE-kmeans architecture feature extraction weighted Euclidean distance formula

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